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01starmpcc /Asclepius-Synthetic-Clinical-Notes Asclepius: Synthetic Clincal Notes & Instruction Dataset Dataset Summary This dataset is official dataset for Asclepius (arxiv) This dataset is composed with Clinical Note - Question - Answer format to build a clinical LLMs. We first synthesized synthetic notes from PMC-Patients case reports with GPT-3.5 Then, we generate instruction-answer pairs for 157k synthetic discharge summaries Supported Tasks This dataset covers below 8 tasks Named Entity… See the full description on the dataset page: https://huggingface.co/datasets/starmpcc/Asclepius-Synthetic-Clinical-Notes.textquestion-answering100K<n<1M118 likes1.2k downloads2y agoHugging Face02ClarusC64 /clinical-perception-intervention-justification-v0.1Clinical Perception–Intervention Justification v0.1 Goal Test whether actions follow directly from perceptual evidence Detect interventions that appear without a visual cause Detect escalation that exceeds image-supported severity What it measures action_without_causeAn intervention is proposed with no supporting image evidence over_escalationThe action exceeds what the visual severity supports justification_okThe response links perception to action explicitly or proportionally How it… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-perception-intervention-justification-v0.1.texttext-generationn<1K0 likes55 downloads9mo agoHugging Face03ClarusC64 /clinical-guideline-strength-correspondence-v0.1 What this dataset tests Guideline strength must track evidence strength. Authority must not exceed data. Why it exists Guidelines often harden too early. Language outruns certainty. This set checks whether recommendation force matches evidence quality. Data format Each row contains evidence_profile guideline_recommendation strength_pressure constraints failure_modes_to_avoid target_behaviors gold_checklist Feed the model evidence_profile… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-guideline-strength-correspondence-v0.1.texttext-classificationn<1K0 likes51 downloads9mo agoHugging Face04ClarusC64 /clinical-quad-endpoint-adjudication-drift-blinding-breach-pressure-governance-submission-v0.1Clarus Clinical Quad Coupling Endpoint Adjudication Integrity v0.1 PurposeDetect adjudication drift driven by four interacting nodes. Quad nodes Endpoint cluster shift Blinding gap or reviewer dominance Operational or vendor process change Governance submission or review pressure InputOne vignette. OutputStrict JSON only. Required keys adjudication_integrity_risk risk_type driver_nodes recommended_action action_detail rationale confidence… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-endpoint-adjudication-drift-blinding-breach-pressure-governance-submission-v0.1.texttext-generationn<1K0 likes42 downloads8mo agoHugging Face05ClarusC64 /clinical-parallel-process-awareness-v0.1Clinical Parallel Process Awareness v0.1 Goal Test if a model can hold separate reasoning streams at once Detect constraint dismissal Detect bleed-over where one stream turns into claims in the other What it measures streams_heldResponse acknowledges and maintains both streams bleed_overConstraint stream improperly becomes a medical claim, or vice versa premature_synthesisResponse forces a single solution that silences one stream assumption_collapseResponse drops a premise entirely Dataset… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-parallel-process-awareness-v0.1.texttext-generationn<1K0 likes41 downloads9mo agoHugging Face06ClarusC64 /clinical_evidence_coherence_breakdown_v0.1Clinical Evidence Coherence Breakdown PurposeDetect when a clinical plan stops matching the evidence. You get evidence signals and a stated plan.You decide if a coherence break exists.You label the breakdown type.You propose the corrective action. Input fields patient_summary evidence_signals stated_diagnosis planned_action Required outputReturn one JSON object coherence_breakyes or no breakdown_typeMust match the allowed list correctionOne sentence Allowed breakdown_type… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical_evidence_coherence_breakdown_v0.1.texttext-classificationn<1K0 likes39 downloads8mo agoHugging Face07ClarusC64 /clinical-anamnesis-fidelity-v0.1Clinical Anamnesis Fidelity v0.1 Goal Test accurate recall and integration of patient history across time Detect distortion, blending, or invention after intervening tasks What it measures assumption_trackingFailure to honor prior stated history fabricationIntroduction of unstated conditions or treatments inference_chainFilling memory gaps with unsupported links Dataset format Each row simulates multi-session history Intervening tasks introduce context pressure Candidate response is… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-anamnesis-fidelity-v0.1.texttext-generationn<1K0 likes35 downloads9mo agoHugging Face08ClarusC64 /clinical-cross-modal-memory-fidelity-v0.1Clinical Cross-Modal Memory Fidelity v0.1 Goal Test whether prior image evidence is recalled accurately over time Detect retroactive distortion driven by later narrative Detect fabrication used to patch memory gaps What it measures memory_driftEarlier image facts are altered or inverted fabricationNew findings are invented at recall cross_modal_consistencyRecalled description matches original image evidence How it works Initial image facts are fixed and explicit Intervening tasks… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-cross-modal-memory-fidelity-v0.1.texttext-generationn<1K0 likes35 downloads9mo agoHugging Face09ClarusC64 /clinical_identity_frame_shift_detection_v0.1Clinical Identity Frame Shift Detection PurposeDetect when the current clinical label no longer fits the evolving evidence. You get: an initial identity label new evidence signals a continuing plan You decide: is the current identity still valid what the new identity should be what action should follow Input fields patient_summary initial_identity new_evidence current_plan Required outputReturn one JSON object identity_validyes or no new_identityshort phrase… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical_identity_frame_shift_detection_v0.1.texttext-classificationn<1K0 likes35 downloads8mo agoHugging Face10ClarusC64 /clinical-quad-dose-selection-suite-v0.1Clarus Clinical Quad Coupling Dose Selection Suite v0.1 What this dataset isThis dataset tests dose selection under four-node coupling pressure. Quad coupling nodes Patient biology and organ reserve Exposure and metabolism constraints Concomitant drugs and interaction risk Governance constraints that limit changes or force holds Input One clinical vignette in prompt OutputReturn strict JSON only. Required output JSON keys recommended_dose_mg dose_schedule hold_or_adjust… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-dose-selection-suite-v0.1.texttext-generationn<1K0 likes30 downloads8mo agoHugging Face11ClarusC64 /clinical_frontier_unknown_detection_v0.1Clinical Frontier Unknown Detection PurposeDetect when a case sits beyond routine clinical knowledge and needs escalation. You receive: patient_summary workup_summary current_plan You decide: frontier_caseyes or no reason_typemust match the allowed list next_stepone sentence Allowed reason_type values no_frontier rare_disease_suspected conflicting_evidence refractory_to_standard atypical_multisystem novel_adverse_event unexplained_biomarker_pattern unknown_unknown… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical_frontier_unknown_detection_v0.1.texttext-classificationn<1K0 likes29 downloads8mo agoHugging Face12ClarusC64 /clinical_false_absence_detection_v0.1GP False Absence Detection PurposeDetect when someone claims an absence of risk but the observed signals contradict it. Input fields claimed_absence observed_signals proposed_action Required outputOne JSON object false_absenceyes or no absence_typeone of the allowed values correct_actionone sentence Run scoringpython scorer.py --predictions predictions.jsonl --test_csv data/test.csv texttext-classificationn<1K0 likes29 downloads8mo agoHugging Face13ClarusC64 /clinical-quad-data-integrity-query-backlog-missingness-governance-threshold-v0.1Clarus Clinical Quad Coupling Data Integrity Query Backlog Missingness Governance Threshold v0.1 What this dataset isThis dataset tests whether a model can detect clinical trial data integrity events driven by four interacting nodes. Quad coupling nodes Query backlog or data flow delay Missingness in critical fields or attachments Conmed or exposure timeline gaps Governance thresholds such as audits, CAPA, freeze deadlines, or reporting cadence Input One vignette in prompt… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-data-integrity-query-backlog-missingness-governance-threshold-v0.1.texttext-generationn<1K0 likes29 downloads8mo agoHugging Face14ClarusC64 /clinical-quad-consent-version-drift-reconsent-gap-enrollment-pressure-governance-audit-v0.1Clarus Clinical Quad Coupling Informed Consent Integrity v0.1 PurposeDetect consent integrity failures driven by four interacting nodes. Quad nodes Consent version drift or addendum mismatch Re-consent gap after material risk change Enrollment pressure or incentives Governance audit or regulator timing InputOne vignette. OutputStrict JSON only. Required keys consent_integrity_risk risk_type driver_nodes recommended_action action_detail rationale confidence… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-consent-version-drift-reconsent-gap-enrollment-pressure-governance-audit-v0.1.texttext-generationn<1K0 likes29 downloads8mo agoHugging Face15ClarusC64 /clinical_epistemic_clarification_v0.1Clinical Epistemic Clarification PurposeDetect when a case requires clarification before action. You receive: current evidence a proposed action You decide: does the case need clarification what clarifying step is required what safe interim action should occur Input fields patient_summary current_evidence proposed_action Required outputReturn one JSON object needs_clarificationyes or no clarifying_stepone sentence safe_interim_actionone sentence Scoring… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical_epistemic_clarification_v0.1.texttext-classificationn<1K0 likes28 downloads8mo agoHugging Face16rvenie /all_clin_rec_minzdrav_ru OCR Clinical Guidelines Dataset of Russian Ministry of Health (актуально на 12 ноября 2025) Описание Данный датасет содержит результаты OCR всех утвержденных Минздравом РФ клинических рекомендаций по состоянию на 12 ноября 2025 года. Каждая запись соответствует одной нозологии и содержит полный текст документа, а также обширные метаданные: код, наименование, возрастная категория, разработчик, статус одобрения, дата публикации и текущее применение. Применение… See the full description on the dataset page: https://huggingface.co/datasets/rvenie/all_clin_rec_minzdrav_ru.texttext-classificationn<1K1 likes27 downloads11mo agoHugging Face17ClarusC64 /clinical-evidence-conclusion-alignment-v0.1 What this dataset tests Clinical conclusions must reflect evidence. Language must track statistics. Why it exists Clinical papers drift at the conclusion. Spin enters here. This set detects misalignment between results and claims. Data format Each row contains trial_result conclusion_statement alignment_pressure constraints failure_modes_to_avoid target_behaviors gold_checklist Feed the model trial_result conclusion_statement Score for… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-evidence-conclusion-alignment-v0.1.texttext-classificationn<1K0 likes27 downloads9mo agoHugging Face18ClarusC64 /clinical_long_silence_integrity_v0.1Clinical Long Silence Integrity Tests whether models maintain safe reasoning after long gaps between contacts. Output JSON integrity gap_risk correct_action Runpython scorer.py --predictions predictions.jsonl --test_csv data/test.csv texttext-classificationn<1K0 likes26 downloads8mo agoHugging Face19ClarusC64 /clinical-quad-enrollment-criteria-drift-site-selection-bias-screening-pressure-v0.1Clarus Clinical Quad Coupling Enrollment Criteria Drift Site Selection Bias Screening Pressure v0.1 PurposeDetect enrollment population drift driven by four interacting nodes. Quad nodes Criteria relaxation or documentation gap Site selection or recruitment bias Screening workflow pressure Governance or interim timing pressure InputOne vignette. OutputStrict JSON only. Required keys enrollment_drift_risk risk_type driver_nodes recommended_action action_detail rationale… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-enrollment-criteria-drift-site-selection-bias-screening-pressure-v0.1.texttext-generationn<1K0 likes26 downloads8mo agoHugging Face20ClarusC64 /regulatory-clinical-translation-integrity-v0.1 What this dataset tests Meaning must survive translation. Regulatory language has limits. Clinical claims must respect them. Why it exists Semantic drift happens at translation boundaries. Conditional becomes absolute. Surrogate becomes outcome. This set detects meaning distortion. Data format Each row contains regulatory_language clinical_evidence translated_claim translation_pressure constraints failure_modes_to_avoid target_behaviors… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/regulatory-clinical-translation-integrity-v0.1.texttext-classificationn<1K0 likes25 downloads9mo agoHugging Face21ClarusC64 /clinical-quad-adjudication-drift-endpoint-reclassification-timing-pressure-v0.1Clarus Clinical Quad Coupling Adjudication Drift Endpoint Reclassification Timing Pressure v0.1 What this dataset isThis dataset tests whether a model can detect endpoint adjudication drift driven by four interacting nodes. Quad coupling nodes Clustered endpoint reclassification Source data delay or missing uploads Exposure or dose documentation gaps Governance or interim analysis pressure Input One vignette OutputReturn strict JSON only. Required output JSON keys… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-adjudication-drift-endpoint-reclassification-timing-pressure-v0.1.texttext-generationn<1K0 likes25 downloads8mo agoHugging Face22ClarusC64 /clinical-decision-constraint-integrity-v0.1Clinical Decision–Constraint Integrity v0.1 What this tests Whether a clinical decision remains structurally coherent when real constraints apply. The model must hold: Medical correctness Practical feasibility Without erasing either. Failure modes constraint_erasedThe decision ignores or deletes the constraint false_resolutionThe response pretends the conflict does not exist coherent_tradeoffThe response names limits and adapts without distortion How it works Decision context defines the… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-decision-constraint-integrity-v0.1.texttext-generationn<1K0 likes24 downloads9mo agoHugging Face23ClarusC64 /clinical-latent-sign-elicitation-v0.1Clinical Latent Sign Elicitation v0.1 Goal Test whether a model can treat weak indicators as weak Detect two failures Over interpretation into certainty Ignoring the weak signal What it measures latent_flaggedResponse marks uncertainty and keeps options open overinterpretsResponse states a definitive conclusion from weak input ignores_signalResponse dismisses or fails to act on weak warning signs next_step_okResponse offers a reasonable plan plus escalation triggers Dataset format Each… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-latent-sign-elicitation-v0.1.texttext-generationn<1K0 likes23 downloads9mo agoHugging Face24ClarusC64 /clinical_structural_drift_detection_v0.1Clinical Structural Drift Detection PurposeDetect when a clinical plan drifts from the evolving patient reality. You get a case with time change signals.You decide if drift exists.You label the drift type.You propose the corrective adjustment. Input fields patient_summary time_series current_plan observed_change drift_signal Required outputReturn one JSON object drift_detectedyes or no drift_typeMust match the allowed list adjustmentOne sentence Allowed drift_type values… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical_structural_drift_detection_v0.1.texttext-classificationn<1K0 likes23 downloads8mo agoHugging Face25ClarusC64 /clinical-quad-recruitment-coherence-mapping-suite-v0.1Clarus Clinical Quad Coupling Recruitment Coherence Mapping Suite v0.1 What this dataset isThis dataset tests whether a model can detect recruitment incoherence under four-node coupling pressure. Quad coupling nodes Biological eligibility definition Concomitant medication or background therapy filters Operational measurement and site process variance Governance constraints limiting protocol flexibility Input One recruitment vignette OutputReturn strict JSON only. Required output… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-recruitment-coherence-mapping-suite-v0.1.texttext-generationn<1K0 likes22 downloads8mo agoHugging Face26ClarusC64 /clinical-regulatory-evidence-correspondence-v0.1 What this dataset tests Regulatory claims must map to evidence scope. Population boundaries matter. Why it exists Regulatory language can drift. Indications expand. Subgroups disappear. This set detects when claims exceed the evidence base. Data format Each row contains evidence_base regulatory_claim correspondence_pressure constraints failure_modes_to_avoid target_behaviors gold_checklist Feed the model evidence_base regulatory_claim Score… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-regulatory-evidence-correspondence-v0.1.texttext-classificationn<1K0 likes20 downloads9mo agoHugging Face27ClarusC64 /clinical_container_inversion_detection_v0.1Clinical Container Inversion Detection PurposeDetect when a clinical system under stress flips from protecting the patient to protecting itself. You receive: system_stressor care_frame proposed_action You output one JSON object: container_inversionyes or no inversion_patternone of the allowed values corrective_actionone sentence restoring patient safety and clinical primacy Allowed inversion_pattern values no_inversion label_anchoring_throughput… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical_container_inversion_detection_v0.1.texttext-classificationn<1K0 likes20 downloads8mo agoHugging Face28ClarusC64 /clinical_safety_coherence_eval_v0.1Clinical Safety Coherence Eval Measures whether model outputs remain clinically safe and coherent under pressure. Output JSON safe failure_mode correct_output Runpython scorer.py --predictions predictions.jsonl --test_csv data/test.csv texttext-classificationn<1K0 likes20 downloads8mo agoHugging Face29ClarusC64 /clinical-outcome-hierarchy-fidelity-v0.1 What this dataset tests Trials have outcome hierarchies. Primary outcomes rule. Secondary outcomes support. Why it exists A common failure is outcome switching. Primary misses get hidden. Secondary wins get promoted. This set detects hierarchy violations in summaries. Data format Each row contains trial_design reported_summary hierarchy_pressure constraints failure_modes_to_avoid target_behaviors gold_checklist Feed the model trial_design… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-outcome-hierarchy-fidelity-v0.1.texttext-classificationn<1K0 likes19 downloads9mo agoHugging Face30ClarusC64 /clinical-harm-benefit-integrity-v0.1 What this dataset tests Safety must constrain conclusions. Benefit claims must stay inside harm evidence. Why it exists A common failure is safety spin. Harms get buried. Language says “safe” or “well tolerated” without support. This set forces explicit harm–benefit balance. Data format Each row contains safety_evidence benefit_evidence summary_claim harm_pressure constraints failure_modes_to_avoid target_behaviors gold_checklist Feed the model… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-harm-benefit-integrity-v0.1.texttext-classificationn<1K0 likes19 downloads9mo agoHugging Face

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