{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_neuroclues","slug":"neuroclues","name":"NeuroClues","type":"product","url":"https://p3lab.com","page_url":"https://unfragile.ai/neuroclues","categories":["data-analysis"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_neuroclues__cap_0","uri":"capability://data.processing.analysis.oculomotor.abnormality.detection.via.eye.tracking","name":"oculomotor-abnormality-detection-via-eye-tracking","description":"Captures and analyzes eye movement patterns (saccades, smooth pursuits, fixations, nystagmus) using infrared corneal reflection tracking at 60-250Hz sampling rates to quantify deviations from normative oculomotor baselines. The system applies machine learning classifiers trained on neurologically-healthy control populations to detect subclinical abnormalities in eye-movement kinematics that precede visible neurological symptoms, enabling detection of early-stage neurodegenerative conditions like Parkinson's, cerebellar ataxia, and progressive supranuclear palsy before conventional clinical signs emerge.","intents":["Detect early-stage Parkinson's disease through quantified saccadic velocity and amplitude changes before motor symptoms appear","Identify cerebellar dysfunction by measuring smooth pursuit gain and fixation stability degradation","Screen for progressive supranuclear palsy by analyzing vertical gaze palsy patterns and saccadic slowing","Quantify oculomotor decline in neurodegenerative disease progression for longitudinal clinical trials","Establish objective baseline metrics for individual patients to track neurological deterioration over time"],"best_for":["Specialized neurology clinics with dedicated neuro-ophthalmology assessment capabilities","Academic medical centers conducting longitudinal neurodegenerative disease research","Clinical trial sites evaluating disease-modifying therapies where early biomarkers are critical"],"limitations":["Requires controlled lighting environment (minimal glare, consistent illumination 300-500 lux) — field deployments or bedside assessments in variable lighting degrade tracking accuracy by 15-40%","Hardware calibration drift occurs every 4-8 hours of continuous use, necessitating recalibration protocols that add 3-5 minutes per session","Generalization limited to populations used in training datasets — performance degrades 8-15% for ethnic groups underrepresented in normative databases","Cannot distinguish between primary oculomotor disorders (e.g., myasthenia gravis affecting extraocular muscles) and central neurological dysfunction without additional clinical context","Requires patient cooperation and ability to follow visual targets — unreliable in patients with severe cognitive impairment, delirium, or inability to maintain fixation"],"requires":["Dedicated eye-tracking hardware (e.g., Tobii Pro Spectrum, EyeLink 1000+) with infrared corneal reflection capability","Controlled clinical environment with standardized lighting and minimal reflective surfaces","Normative reference database for patient age, ethnicity, and baseline neurological status","Trained technician for hardware setup, calibration, and quality assurance of tracking data","Integration with EHR system for storing and retrieving patient baseline metrics and longitudinal comparisons"],"input_types":["real-time eye-gaze coordinates (x, y pixel positions at 60-250Hz)","pupil diameter measurements","visual stimulus presentation (fixation targets, smooth pursuit targets, saccadic targets)","patient demographic metadata (age, sex, ethnicity, known neurological diagnoses)"],"output_types":["quantified oculomotor metrics (saccadic velocity, amplitude, latency; smooth pursuit gain; fixation stability variance)","deviation scores vs. normative baselines (z-scores, percentiles)","diagnostic probability estimates for specific neurological conditions","longitudinal trend reports showing oculomotor decline over months/years","structured clinical reports suitable for EHR integration"],"categories":["data-processing-analysis","medical-diagnostics","biomarker-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_neuroclues__cap_1","uri":"capability://data.processing.analysis.longitudinal.oculomotor.decline.tracking","name":"longitudinal-oculomotor-decline-tracking","description":"Stores baseline oculomotor metrics for individual patients and compares subsequent assessments against personalized baselines using statistical process control methods (e.g., exponentially-weighted moving average, control charts) to detect statistically-significant decline trajectories. The system generates alerts when oculomotor metrics deviate beyond patient-specific confidence intervals, enabling clinicians to quantify disease progression velocity and adjust therapeutic interventions based on objective biomarker trends rather than subjective symptom reports.","intents":["Track Parkinson's disease progression velocity by monitoring saccadic velocity decline over 6-12 month intervals","Quantify treatment efficacy by comparing oculomotor metrics before and after initiating disease-modifying therapies","Identify accelerated neurodegeneration requiring escalation of care or clinical trial enrollment","Generate objective evidence of disease progression for disability insurance or clinical trial eligibility documentation","Establish patient-specific decline rates to predict future functional decline and plan long-term care needs"],"best_for":["Neurology clinics managing chronic neurodegenerative diseases requiring objective progression monitoring","Clinical trial sites evaluating disease-modifying therapies where objective biomarkers replace subjective outcome measures","Patients with early-stage disease where subjective symptom progression is minimal but objective biomarker decline is detectable"],"limitations":["Requires minimum 2-3 assessments separated by 3-6 months to establish reliable decline trajectories — insufficient for acute diagnostic decisions","Oculomotor metrics show high inter-individual variability in decline rates; population-level decline curves may not predict individual patient trajectories with high precision","Confounding factors (fatigue, medication timing, sleep quality, caffeine intake) introduce day-to-day variability of 5-15% in oculomotor metrics, obscuring true disease progression signal","Requires consistent hardware and calibration across longitudinal assessments — hardware upgrades or recalibration procedures can introduce systematic shifts in metrics","No validated clinical decision thresholds for when oculomotor decline warrants therapeutic intervention changes"],"requires":["Baseline oculomotor assessment within first clinical visit","Standardized follow-up assessment protocol (e.g., same time of day, consistent medication timing, controlled environment)","Persistent patient identifier linking assessments across time","Statistical process control algorithms for detecting significant deviation from baseline","EHR integration for storing and retrieving longitudinal metric history"],"input_types":["baseline oculomotor metrics from initial assessment","follow-up oculomotor measurements at defined intervals (3-6 months)","patient metadata (age, disease duration, medication regimen, clinical stage)","assessment context (time of day, medication timing, environmental conditions)"],"output_types":["decline trajectory estimates (slope of oculomotor metric change per month/year)","statistical significance tests comparing current vs. baseline metrics","alert notifications when decline exceeds patient-specific thresholds","longitudinal graphs showing oculomotor metric trends over time","predicted future decline projections (e.g., estimated time to functional threshold)"],"categories":["data-processing-analysis","planning-reasoning","medical-diagnostics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_neuroclues__cap_2","uri":"capability://data.processing.analysis.multimodal.neurological.abnormality.classification","name":"multimodal-neurological-abnormality-classification","description":"Integrates oculomotor metrics with optional supplementary neurological data (tremor accelerometry, gait kinematics, cognitive reaction times) into ensemble machine learning classifiers (random forests, gradient boosting, neural networks) trained on clinically-diagnosed patient cohorts to generate probabilistic diagnoses for specific neurological conditions. The system outputs condition-specific probability scores (e.g., 78% Parkinson's, 12% essential tremor, 10% other) with confidence intervals, enabling clinicians to rank differential diagnoses and prioritize confirmatory testing.","intents":["Generate differential diagnosis probabilities for patients with suspected Parkinson's disease vs. essential tremor vs. atypical parkinsonian syndromes","Identify patients with subclinical cerebellar dysfunction who may benefit from early intervention","Classify neurodegenerative disease subtypes (e.g., akinetic-rigid vs. tremor-dominant Parkinson's) based on oculomotor phenotypes","Predict which patients with mild cognitive impairment will progress to Alzheimer's disease within 2-3 years","Rank diagnostic likelihood to guide ordering of confirmatory tests (MRI, PET, genetic testing) in resource-constrained settings"],"best_for":["General neurology clinics evaluating patients with suspected neurodegenerative disease where diagnostic uncertainty is high","Primary care settings lacking neuro-ophthalmology expertise, seeking objective diagnostic support","Resource-limited healthcare systems where expensive confirmatory tests (amyloid PET, tau biomarkers) must be prioritized based on pre-test probability"],"limitations":["Classification accuracy varies by condition and patient population — reported 75-92% sensitivity/specificity for Parkinson's disease but only 60-75% for atypical parkinsonian syndromes","Training datasets typically skew toward advanced disease stages; early-stage disease classification accuracy is 10-20% lower than advanced-stage","Ethnic and demographic representation in training cohorts is limited — classifier performance degrades 8-15% for underrepresented populations","Cannot distinguish between primary neurological disease and secondary oculomotor abnormalities from non-neurological causes (e.g., ophthalmologic disease, medication side effects)","Requires integration of multiple data modalities (oculomotor + tremor + gait) for optimal performance; oculomotor-only classification is 5-10% less accurate"],"requires":["Trained machine learning classifier models with documented performance metrics on held-out test sets","Training cohort with clinically-confirmed diagnoses (ideally neuropathologically-confirmed for neurodegenerative diseases)","Standardized feature extraction pipeline converting raw oculomotor data to classifier input features","Calibration data for translating classifier outputs to clinically-meaningful probability estimates","Optional: supplementary sensor data (accelerometry, gait analysis) for multimodal classification"],"input_types":["oculomotor metrics (saccadic velocity, smooth pursuit gain, fixation stability, etc.)","optional tremor accelerometry data","optional gait kinematics (stride length, cadence, variability)","optional cognitive reaction time measurements","patient demographics (age, sex, disease duration, symptom onset pattern)"],"output_types":["condition-specific probability scores (e.g., P(Parkinson's) = 0.78, P(essential tremor) = 0.12)","confidence intervals around probability estimates","ranked differential diagnosis list","feature importance scores showing which oculomotor metrics most strongly support each diagnosis","structured clinical report with diagnostic recommendations and suggested confirmatory tests"],"categories":["data-processing-analysis","planning-reasoning","medical-diagnostics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_neuroclues__cap_3","uri":"capability://data.processing.analysis.real.time.eye.tracking.data.acquisition.and.preprocessing","name":"real-time-eye-tracking-data-acquisition-and-preprocessing","description":"Captures raw eye-gaze coordinates and pupil diameter from infrared corneal reflection eye-tracker hardware at 60-250Hz sampling rates, applies real-time preprocessing (blink detection, saccade detection via velocity thresholding, fixation clustering, outlier removal) to clean noisy tracking data, and streams preprocessed gaze events to downstream analysis pipelines. The system implements hardware-specific calibration routines (9-point or 13-point grid calibration) and validates tracking quality metrics (gaze accuracy, precision, data loss rate) before accepting data for clinical analysis.","intents":["Acquire high-fidelity eye-gaze data from patients during standardized oculomotor testing protocols","Detect and filter blink artifacts that would corrupt oculomotor metric calculations","Identify saccadic eye movements and separate them from smooth pursuit and fixation phases","Validate tracking quality and alert technicians when recalibration is needed before clinical assessment","Stream preprocessed gaze data to real-time visualization dashboards for technician monitoring during assessment"],"best_for":["Clinical eye-tracking labs with trained technicians performing standardized oculomotor assessments","Research institutions conducting oculomotor studies requiring high-quality gaze data","Specialized neurology clinics integrating eye-tracking into diagnostic workflows"],"limitations":["Tracking accuracy degrades 15-40% in non-ideal lighting conditions (glare, shadows, variable illumination) — requires controlled clinical environment","Hardware calibration drift accumulates over 4-8 hours of continuous use, introducing systematic gaze position errors of 0.5-2 degrees","Blink detection algorithms may miss partial blinks or blinks with slow eyelid closure, introducing artifacts into fixation analysis","Saccade detection via velocity thresholding is sensitive to threshold parameter selection — suboptimal thresholds miss small saccades or misclassify smooth pursuit as saccades","Data loss during blinks (typically 100-300ms per blink) creates gaps in gaze trajectory that must be handled by interpolation or exclusion"],"requires":["Eye-tracking hardware with infrared corneal reflection capability (e.g., Tobii Pro Spectrum, EyeLink 1000+) and manufacturer SDK","Calibration target display (monitor or projection system) with precise geometric calibration","Real-time data processing pipeline (typically 50-100ms latency) for blink/saccade detection and outlier removal","Quality assurance metrics (gaze accuracy, precision, data loss rate) with defined acceptance thresholds","Technician training on calibration procedures and troubleshooting tracking failures"],"input_types":["raw eye-gaze coordinates (x, y pixel positions) at 60-250Hz","pupil diameter measurements","eye-tracking confidence/validity flags from hardware","visual stimulus timing information (when targets appear/disappear)","blink detection signals (optional, from hardware or software detection)"],"output_types":["preprocessed gaze coordinates with blink periods marked as invalid","saccade events (onset time, offset time, amplitude, peak velocity, direction)","fixation events (onset time, duration, gaze position, stability metrics)","smooth pursuit segments (onset time, duration, target velocity, pursuit gain)","quality metrics (gaze accuracy, precision, data loss percentage, tracking confidence)","real-time visualization of gaze position overlaid on stimulus display"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_neuroclues__cap_4","uri":"capability://automation.workflow.standardized.oculomotor.testing.protocol.execution","name":"standardized-oculomotor-testing-protocol-execution","description":"Implements standardized visual stimulus presentation sequences (fixation tasks, smooth pursuit tasks, saccadic tasks, optokinetic nystagmus tasks) with precise timing control and stimulus geometry to elicit reproducible oculomotor responses across patients and assessment sessions. The system presents calibrated visual targets at defined eccentricities and velocities, records stimulus timing metadata, and ensures consistent task execution across different clinical sites through protocol validation and technician training modules.","intents":["Present standardized fixation targets to measure fixation stability and microsaccade characteristics","Display moving pursuit targets at controlled velocities (10-40 deg/sec) to assess smooth pursuit gain and latency","Execute saccadic tasks with targets at defined eccentricities (5, 10, 20 degrees) to measure saccadic velocity and accuracy","Present optokinetic nystagmus stimuli (rotating drum or moving grating) to assess vestibulo-ocular reflex function","Ensure reproducible stimulus presentation across multiple assessment sessions for longitudinal tracking"],"best_for":["Specialized neurology clinics performing standardized oculomotor assessments for research or clinical decision-making","Multi-site clinical trials requiring consistent stimulus presentation across geographically-distributed sites","Research institutions conducting oculomotor studies with strict protocol adherence requirements"],"limitations":["Requires calibrated display hardware with known refresh rate and pixel-to-degree conversion — non-standard displays introduce stimulus geometry errors","Patient fatigue and attention fluctuations affect oculomotor responses, particularly for tasks lasting >10 minutes — requires careful task ordering and rest periods","Stimulus presentation timing jitter (typically 1-5ms) can introduce variability in oculomotor response latencies, particularly for saccadic tasks","Cannot account for individual differences in visual acuity, refractive error, or ocular media opacity that affect stimulus visibility","Protocol compliance varies across sites and technicians — requires ongoing training and quality assurance monitoring"],"requires":["Calibrated display hardware (monitor or projection system) with known refresh rate (60Hz minimum, 120Hz+ preferred) and geometric calibration","Stimulus presentation software with precise timing control (millisecond-level accuracy) and stimulus geometry validation","Standardized protocol documentation with detailed task descriptions, stimulus parameters, and execution order","Technician training program with competency assessment and ongoing quality assurance monitoring","Protocol validation procedures to ensure consistent stimulus presentation across sites and hardware configurations"],"input_types":["protocol specification (task sequence, stimulus parameters, timing)","display hardware configuration (resolution, refresh rate, viewing distance)","patient readiness indicators (visual acuity, ability to follow instructions)","technician input (task start/stop commands, protocol deviations)"],"output_types":["stimulus presentation logs (task timing, stimulus positions, stimulus velocities)","eye-gaze data synchronized with stimulus timing","protocol compliance reports (tasks completed, deviations from protocol)","oculomotor response metrics extracted from synchronized gaze and stimulus data","quality assurance metrics (stimulus presentation accuracy, timing jitter)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_neuroclues__cap_5","uri":"capability://data.processing.analysis.normative.baseline.comparison.and.z.score.calculation","name":"normative-baseline-comparison-and-z-score-calculation","description":"Compares individual patient oculomotor metrics against age-stratified, ethnicity-stratified normative reference databases using z-score calculations to quantify deviation magnitude from healthy population norms. The system applies demographic-specific normalization (accounting for age-related oculomotor decline, sex differences, ethnic variation) and generates percentile ranks and confidence intervals around deviation scores, enabling clinicians to interpret whether observed oculomotor abnormalities are statistically significant or within normal variation.","intents":["Calculate z-scores for individual oculomotor metrics (saccadic velocity, smooth pursuit gain) relative to age-matched healthy controls","Generate percentile ranks showing where patient metrics fall within healthy population distribution","Identify which specific oculomotor metrics deviate most significantly from normative baselines","Account for age-related oculomotor decline when interpreting metrics in older patients","Detect ethnic-specific oculomotor variations to avoid misclassifying normal ethnic variation as pathological"],"best_for":["Clinical neurology practices seeking objective interpretation of oculomotor metrics relative to population norms","Research institutions comparing patient cohorts to healthy control populations","Multi-site studies requiring standardized normative comparison across geographically-distributed sites"],"limitations":["Normative databases are limited to specific age ranges and ethnic groups — extrapolation to underrepresented populations introduces systematic bias","Age-related oculomotor decline shows high inter-individual variability — population-level age correction may not accurately capture individual aging trajectories","Normative databases typically exclude patients with subclinical neurological abnormalities, potentially inflating apparent abnormality thresholds","Z-score interpretation assumes normal distribution of oculomotor metrics, which may not hold for metrics with skewed distributions (e.g., smooth pursuit gain)","Requires large normative cohorts (n>100 per age/ethnicity stratum) for stable baseline estimates — smaller cohorts introduce sampling variability"],"requires":["Normative reference database with age-stratified and ethnicity-stratified oculomotor metrics (mean, standard deviation) from healthy control populations","Demographic metadata for each patient (age, sex, ethnicity, handedness)","Statistical methods for z-score calculation and confidence interval estimation","Validation of normative database representativeness and absence of subclinical abnormalities in control population"],"input_types":["individual patient oculomotor metrics (saccadic velocity, smooth pursuit gain, fixation stability, etc.)","patient demographics (age, sex, ethnicity)","normative reference database (mean and SD for each metric by age/ethnicity stratum)"],"output_types":["z-scores for each oculomotor metric (deviation in standard deviations from population mean)","percentile ranks (e.g., patient metric at 5th percentile of healthy population)","confidence intervals around z-scores","summary abnormality score (e.g., number of metrics >2 SD from mean)","visual reports showing patient metrics relative to normative distribution"],"categories":["data-processing-analysis","medical-diagnostics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_neuroclues__cap_6","uri":"capability://tool.use.integration.ehr.integration.and.clinical.report.generation","name":"ehr-integration-and-clinical-report-generation","description":"Exports oculomotor assessment results (metrics, diagnoses, longitudinal trends) in standardized clinical report formats compatible with electronic health record systems, including structured data fields (FHIR-compatible observations) and human-readable narrative summaries. The system generates PDF reports suitable for clinician review and EHR import, with embedded visualizations (metric trends, diagnostic probability charts) and recommendations for follow-up testing or therapeutic intervention.","intents":["Export oculomotor metrics and diagnostic results to EHR in structured format for integration with patient medical record","Generate clinical reports summarizing assessment findings, diagnostic impressions, and recommendations for clinician review","Create longitudinal trend reports showing oculomotor metric changes over multiple assessment sessions","Produce diagnostic probability reports ranking differential diagnoses for clinical decision-making","Generate research-grade reports with detailed methodology and quality assurance metrics for clinical trial documentation"],"best_for":["Neurology clinics with EHR systems requiring structured data import of oculomotor assessment results","Multi-site clinical trials needing standardized report generation across geographically-distributed sites","Healthcare systems seeking to integrate objective oculomotor biomarkers into clinical workflows"],"limitations":["EHR integration requires custom development for each EHR vendor (Epic, Cerner, Athena, etc.) — no universal standard for oculomotor data import","FHIR standards for oculomotor observations are still evolving — current implementations may require custom FHIR extensions","Clinical report generation requires clinician review and sign-off before EHR import — cannot be fully automated without regulatory approval","PDF reports lack machine-readable structure for secondary analysis or data aggregation across patients","Requires HIPAA-compliant data handling and secure transmission protocols for EHR integration"],"requires":["EHR system with API or HL7/FHIR integration capability","Structured data export format (FHIR Observation resources or vendor-specific formats)","Clinical report template with standardized sections (findings, interpretation, recommendations)","PDF generation library with support for embedded visualizations and charts","HIPAA-compliant data transmission and storage infrastructure"],"input_types":["oculomotor metrics (saccadic velocity, smooth pursuit gain, fixation stability, etc.)","diagnostic results (condition probabilities, confidence intervals)","longitudinal trend data (metric changes over time)","patient demographics and clinical context","assessment quality metrics (tracking accuracy, data loss rate)"],"output_types":["structured FHIR Observation resources for EHR import","human-readable clinical reports (PDF format)","HL7 v2 messages for legacy EHR systems","longitudinal trend visualizations (graphs, charts)","diagnostic probability reports with recommendations","research-grade reports with detailed methodology and QA metrics"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_neuroclues__cap_7","uri":"capability://data.processing.analysis.quality.assurance.and.data.validity.monitoring","name":"quality-assurance-and-data-validity-monitoring","description":"Monitors eye-tracking data quality metrics in real-time (gaze accuracy, precision, data loss rate, tracking confidence) and flags assessment sessions with suboptimal data quality that may compromise diagnostic validity. The system implements automated quality checks (e.g., gaze accuracy >1.5 degrees triggers recalibration alert, data loss >10% triggers session rejection) and generates quality assurance reports documenting tracking performance and protocol compliance for each assessment session.","intents":["Detect hardware calibration drift during assessment and alert technicians to recalibrate before continuing","Identify sessions with excessive data loss (blinks, tracking failures) that compromise metric reliability","Validate that gaze accuracy meets clinical standards before accepting data for diagnostic analysis","Document quality metrics for each assessment session to support clinical decision-making confidence","Generate quality assurance reports for multi-site clinical trials to ensure consistent data quality across sites"],"best_for":["Clinical eye-tracking labs requiring real-time quality monitoring during patient assessments","Multi-site clinical trials needing standardized quality assurance across geographically-distributed sites","Research institutions conducting oculomotor studies with strict data quality requirements"],"limitations":["Quality thresholds (e.g., gaze accuracy >1.5 degrees) are somewhat arbitrary and may vary by clinical application — no universal consensus on acceptable quality standards","Real-time quality monitoring adds computational overhead (typically 5-10% CPU increase) that may impact data acquisition latency","Quality metrics are hardware-dependent — different eye-tracking systems report quality metrics differently, complicating multi-site comparisons","Automated quality checks may be overly conservative (rejecting valid data) or overly permissive (accepting marginal data) depending on threshold calibration","Quality assurance reports require human interpretation — automated flags may not capture context-specific quality issues"],"requires":["Real-time quality metric calculation from eye-tracking hardware (gaze accuracy, precision, data loss rate)","Defined quality thresholds for each metric (e.g., gaze accuracy <1.5 degrees, data loss <10%)","Automated alerting system to notify technicians of quality issues during assessment","Quality assurance report generation with session-level quality metrics","Validation of quality thresholds against clinical outcomes (e.g., correlation between data quality and diagnostic accuracy)"],"input_types":["raw eye-tracking data (gaze coordinates, pupil diameter, tracking confidence)","calibration validation data (gaze accuracy at known target positions)","session metadata (duration, number of blinks, tracking failures)","quality threshold parameters (acceptable gaze accuracy, data loss rate, etc.)"],"output_types":["real-time quality alerts (e.g., 'Gaze accuracy degraded to 2.1 degrees — recalibration recommended')","session-level quality metrics (mean gaze accuracy, precision, data loss percentage)","quality assurance reports documenting tracking performance","pass/fail determination for session data validity","recommendations for data exclusion or reacquisition if quality is suboptimal"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_neuroclues__cap_8","uri":"capability://automation.workflow.technician.training.and.competency.assessment","name":"technician-training-and-competency-assessment","description":"Provides structured training modules for eye-tracking technicians covering hardware operation, calibration procedures, patient interaction, protocol execution, and troubleshooting common tracking failures. The system includes competency assessment tools (simulated patient scenarios, calibration accuracy tests, protocol compliance quizzes) to validate technician proficiency before independent clinical assessment execution, with ongoing competency monitoring through quality assurance metrics.","intents":["Train new technicians on eye-tracking hardware setup, calibration, and troubleshooting procedures","Validate technician competency in executing standardized oculomotor testing protocols","Assess technician ability to identify and resolve tracking failures during patient assessments","Monitor ongoing technician competency through quality assurance metrics (calibration accuracy, protocol compliance)","Ensure consistent assessment quality across multiple technicians and clinical sites"],"best_for":["Clinical eye-tracking labs with multiple technicians requiring standardized training and competency assessment","Multi-site clinical trials needing to ensure consistent technician proficiency across geographically-distributed sites","Healthcare systems integrating eye-tracking into clinical workflows and requiring staff training programs"],"limitations":["Training effectiveness varies by technician background and learning style — standardized training may not accommodate individual learning needs","Competency assessment tools may not fully capture real-world troubleshooting ability in complex patient scenarios","Ongoing competency monitoring requires regular quality assurance reviews, adding administrative burden","Training materials require regular updates as hardware and protocols evolve","No universal standards for eye-tracking technician competency — training content varies across institutions"],"requires":["Structured training curriculum covering hardware operation, calibration, protocol execution, and troubleshooting","Competency assessment tools (simulated scenarios, practical tests, knowledge quizzes)","Documentation of training completion and competency assessment results","Ongoing quality assurance monitoring to track technician performance metrics","Mechanism for retraining or remediation if technician competency declines"],"input_types":["training curriculum content (hardware manuals, protocol documentation, troubleshooting guides)","competency assessment scenarios (simulated patient cases, calibration challenges)","technician performance data (calibration accuracy, protocol compliance, quality metrics)","feedback from supervising clinicians"],"output_types":["training completion certificates","competency assessment scores and pass/fail determinations","individual technician performance dashboards (quality metrics over time)","recommendations for retraining or skill development","multi-site technician competency comparisons"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Dedicated eye-tracking hardware (e.g., Tobii Pro Spectrum, EyeLink 1000+) with infrared corneal reflection capability","Controlled clinical environment with standardized lighting and minimal reflective surfaces","Normative reference database for patient age, ethnicity, and baseline neurological status","Trained technician for hardware setup, calibration, and quality assurance of tracking data","Integration with EHR system for storing and retrieving patient baseline metrics and longitudinal comparisons","Baseline oculomotor assessment within first clinical visit","Standardized follow-up assessment protocol (e.g., same time of day, consistent medication timing, controlled environment)","Persistent patient identifier linking assessments across time","Statistical process control algorithms for detecting significant deviation from baseline","EHR integration for storing and retrieving longitudinal metric history"],"failure_modes":["Requires controlled lighting environment (minimal glare, consistent illumination 300-500 lux) — field deployments or bedside assessments in variable lighting degrade tracking accuracy by 15-40%","Hardware calibration drift occurs every 4-8 hours of continuous use, necessitating recalibration protocols that add 3-5 minutes per session","Generalization limited to populations used in training datasets — performance degrades 8-15% for ethnic groups underrepresented in normative databases","Cannot distinguish between primary oculomotor disorders (e.g., myasthenia gravis affecting extraocular muscles) and central neurological dysfunction without additional clinical context","Requires patient cooperation and ability to follow visual targets — unreliable in patients with severe cognitive impairment, delirium, or inability to maintain fixation","Requires minimum 2-3 assessments separated by 3-6 months to establish reliable decline trajectories — insufficient for acute diagnostic decisions","Oculomotor metrics show high inter-individual variability in decline rates; population-level decline curves may not predict individual patient trajectories with high precision","Confounding factors (fatigue, medication timing, sleep quality, caffeine intake) introduce day-to-day variability of 5-15% in oculomotor metrics, obscuring true disease progression signal","Requires consistent hardware and calibration across longitudinal assessments — hardware upgrades or recalibration procedures can introduce systematic shifts in metrics","No validated clinical decision thresholds for when oculomotor decline warrants therapeutic intervention changes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:31.858Z","last_scraped_at":"2026-04-05T13:23:42.560Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=neuroclues","compare_url":"https://unfragile.ai/compare?artifact=neuroclues"}},"signature":"hHsKAMy/SDHEmEDvz84SLQKSoAXRVu3xpz5rdOAOUsmGHzb3MYTrL5J279NM2i7Rl/FYT7nYRJ/ajJ4CX8WiBg==","signedAt":"2026-06-20T22:45:02.193Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/neuroclues","artifact":"https://unfragile.ai/neuroclues","verify":"https://unfragile.ai/api/v1/verify?slug=neuroclues","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}