Datasets:

ulamai
/

UnsolvedMedical

#!/usr/bin/env python3
"""
UnsolvedMedical v3.0.0 — Frontier AI Study-Design & Hypothesis Benchmark Suite (`eval/`).
Modes:
  1. `python3 scripts/evaluate_ai_benchmark.py --build`
     Extracts the stratified 500-problem Frontier Benchmark (`eval/benchmark_v3.jsonl`,
     25 problems per medical category x 20 categories, balanced across all 20 question types)
     and computes baseline reference scores in `eval/benchmark_summary.json`.
  2. `python3 scripts/evaluate_ai_benchmark.py --predictions <model_outputs.jsonl>`
     Evaluates an AI model's study-design, evidence-gap, PICO-estimand, and hypothesis predictions
     against `eval/benchmark_v3.jsonl`.
"""

import argparse
import json
import re
from collections import Counter, defaultdict
from pathlib import Path

ROOT = Path(__file__).resolve().parent.parent
BENCHMARK_PATH = ROOT / "eval" / "benchmark_v3.jsonl"
SUMMARY_PATH = ROOT / "eval" / "benchmark_summary.json"


def tokenize(text: str) -> set[str]:
    return {w.lower() for w in re.findall(r"[A-Za-z0-9]{3,}", text or "")}


def set_f1(pred_set: set, gold_set: set) -> float:
    if not pred_set and not gold_set:
        return 1.0
    if not pred_set or not gold_set:
        return 0.0
    inter = len(pred_set & gold_set)
    if inter == 0:
        return 0.0
    prec = inter / len(pred_set)
    rec = inter / len(gold_set)
    return 2 * prec * rec / (prec + rec)


def score_prediction(pred: dict, ref: dict) -> dict[str, float]:
    """Score a single model prediction dictionary against a benchmark reference_target."""
    # 1. Study Mode Selection F1
    pred_modes = set(pred.get("acceptable_study_modes") or [])
    gold_modes = set(ref.get("acceptable_study_modes") or [])
    study_mode_f1 = set_f1(pred_modes, gold_modes) * 100.0

    # 2. Evidence Gap Diagnosis F1
    pred_gaps = set(pred.get("evidence_gap_types") or [])
    gold_gaps = set(ref.get("evidence_gap_types") or [])
    evidence_gap_f1 = set_f1(pred_gaps, gold_gaps) * 100.0

    # 3. PICO & Estimand Specification Score
    gold_pico_tokens = tokenize(
        f"{ref.get('target_intervention_or_predictor', '')} {ref.get('target_comparator', '')} "
        f"{ref.get('critical_outcome', '')} {' '.join(ref.get('minimum_conditions', []))}"
    )
    pred_pico_tokens = tokenize(
        f"{pred.get('target_intervention_or_predictor', '')} {pred.get('target_comparator', '')} "
        f"{pred.get('critical_outcome', '')} {pred.get('settlement_design_summary', '')} "
        f"{' '.join(pred.get('minimum_conditions', []))}"
    )
    pico_estimand_score = set_f1(pred_pico_tokens, gold_pico_tokens) * 100.0

    # 4. Falsifiable Competing Hypotheses Coverage Score
    gold_hyp_tokens = tokenize(
        " ".join(
            f"{h.get('description', '')} {' '.join(h.get('predicted_observations', []))}"
            for h in (ref.get("competing_hypotheses") or [])
        )
    )
    pred_hyps = pred.get("competing_hypotheses") or []
    pred_hyp_tokens = tokenize(
        " ".join(
            (f"{h.get('description', '')} {' '.join(h.get('predicted_observations', []))}" if isinstance(h, dict) else str(h))
            for h in pred_hyps
        )
    )
    has_two_hyps = 1.0 if len(pred_hyps) >= 2 else (0.5 if len(pred_hyps) == 1 else 0.0)
    hypothesis_score = (0.75 * set_f1(pred_hyp_tokens, gold_hyp_tokens) + 0.25 * has_two_hyps) * 100.0

    composite = (
        0.30 * study_mode_f1
        + 0.25 * evidence_gap_f1
        + 0.25 * pico_estimand_score
        + 0.20 * hypothesis_score
    )
    return {
        "study_mode_f1": round(study_mode_f1, 2),
        "evidence_gap_f1": round(evidence_gap_f1, 2),
        "pico_estimand_score": round(pico_estimand_score, 2),
        "hypothesis_coverage_score": round(hypothesis_score, 2),
        "overall_composite_score": round(composite, 2),
    }


def build_benchmark() -> None:
    problems = json.loads((ROOT / "problems.json").read_text(encoding="utf-8"))
    by_cat = defaultdict(list)
    for p in problems:
        if p.get("is_gold_seed"):
            by_cat[p["category_num"]].append(p)

    benchmark_rows = []
    qtype_counter = Counter()
    for cat_num in range(1, 21):
        cands = by_cat[cat_num]
        # Select 25 problems per category balancing question types and difficulty
        cands_sorted = sorted(
            cands,
            key=lambda r: (qtype_counter[r["question_type"]], -r["difficulty_level_id"], r["problem_number"]),
        )
        chosen = []
        used_ids = set()
        for _ in range(25):
            best = min(
                (c for c in cands if c["id"] not in used_ids),
                key=lambda r: (qtype_counter[r["question_type"]], -r["difficulty_level_id"], r["problem_number"]),
            )
            chosen.append(best)
            used_ids.add(best["id"])
            qtype_counter[best["question_type"]] += 1

        chosen.sort(key=lambda r: r["problem_number"])
        for r in chosen:
            benchmark_rows.append({
                "benchmark_id": f"UMED-BENCH-{len(benchmark_rows) + 1:04d}",
                "problem_id": r["id"],
                "category_id": r["category_id"],
                "question_type": r["question_type"],
                "research_difficulty": r["research_difficulty"],
                "prompt_input": {
                    "title": r["title"],
                    "statement": r["statement"],
                    "clean_statement": r["clean_statement"],
                    "background": r["background"],
                    "condition": r["condition_or_health_state"]["name"],
                    "population": r["population"]["description"],
                    "time_horizon": r["time_horizon"],
                    "known_partial_results": [k["summary"] for k in r["known_partial_results"]],
                    "source_citation": r["source_citation"],
                },
                "reference_target": {
                    "question_framework_type": r["question_framework"]["type"],
                    "target_intervention_or_predictor": r["intervention_exposure_test_or_predictor"]["name"],
                    "target_kind": r["intervention_exposure_test_or_predictor"]["kind"],
                    "target_comparator": r["comparator_or_reference"],
                    "critical_outcome": r["outcomes"][0]["name"],
                    "safety_outcome": r["outcomes"][1]["name"] if len(r["outcomes"]) > 1 else "",
                    "evidence_gap_types": r["evidence_gap_types"],
                    "acceptable_study_modes": r["what_would_settle_it"]["acceptable_study_modes"],
                    "settlement_design_summary": r["what_would_settle_it"]["summary"],
                    "minimum_conditions": r["what_would_settle_it"]["minimum_conditions"],
                    "competing_hypotheses": r["competing_hypotheses_or_interpretations"],
                    "active_clinical_trial": r["status_assessment"]["active_research"][0] if r["status_assessment"]["active_research"] else "",
                },
            })

    BENCHMARK_PATH.parent.mkdir(parents=True, exist_ok=True)
    with BENCHMARK_PATH.open("w", encoding="utf-8") as f:
        for row in benchmark_rows:
            f.write(json.dumps(row, ensure_ascii=False, separators=(",", ":")) + "\n")

    # Compute 3 reference baselines on the 500 benchmark tasks:
    # 1. Majority-Class / Uninformed Clinical Baseline (always predicts standard RCT + general gap)
    # 2. Lexical / Prompt-Extraction Heuristic Baseline (infers study mode & PICO from prompt text keywords)
    # 3. Domain-Stratified Retrieval Baseline (retrieves nearest neighbor in same category)
    majority_scores = []
    lexical_scores = []
    retrieval_scores = []

    for idx, row in enumerate(benchmark_rows):
        ref = row["reference_target"]
        p_in = row["prompt_input"]
        stmt_low = p_in["statement"].lower()

        # Baseline 1: Majority-Class RCT Baseline
        maj_pred = {
            "acceptable_study_modes": ["randomized_trial", "prospective_cohort"],
            "evidence_gap_types": ["no_adequately_powered_study", "uncertain_long_term_effects"],
            "target_intervention_or_predictor": p_in["condition"],
            "target_comparator": "Standard of care",
            "critical_outcome": "Overall clinical survival and morbidity",
            "minimum_conditions": [f"Follow-up over {p_in['time_horizon']}"],
            "competing_hypotheses": [
                {"description": f"Intervention improves outcomes over {p_in['time_horizon']}", "predicted_observations": ["Superior primary outcome"]},
            ],
        }
        majority_scores.append(score_prediction(maj_pred, ref))

        # Baseline 2: Lexical Clinical Keyword Heuristic Baseline
        inferred_modes = ["prospective_cohort"]
        if any(w in stmt_low for w in ("diagnostic", "accuracy", "sensitivity", "screening", "positivity")):
            inferred_modes = ["diagnostic_accuracy_study", "clinical_utility_study"]
        elif any(w in stmt_low for w in ("prognostic", "stratify", "risk prediction", "calibration")):
            inferred_modes = ["prognostic_validation", "prospective_cohort"]
        elif any(w in stmt_low for w in ("pathway", "molecular", "cellular", "mechanistic")):
            inferred_modes = ["mechanistic_human_study", "prospective_cohort"]
        elif any(w in stmt_low for w in ("implementation", "health system", "population", "disparities", "scaling")):
            inferred_modes = ["pragmatic_trial", "quasi_experimental_study"]
        else:
            inferred_modes = ["randomized_trial", "pragmatic_trial"]

        inferred_gaps = ["no_adequately_powered_study", "uncertain_generalizability"]
        if "diagnostic" in stmt_low or "measurement" in stmt_low:
            inferred_gaps = ["measurement_invalidity", "uncertain_reference_standard"]
        elif "prognos" in stmt_low or "classification" in stmt_low:
            inferred_gaps = ["model_not_externally_validated", "conflicting_interpretations"]

        lex_pred = {
            "acceptable_study_modes": inferred_modes,
            "evidence_gap_types": inferred_gaps,
            "target_intervention_or_predictor": p_in["clean_statement"],
            "target_comparator": "Standard clinical care",
            "critical_outcome": p_in["clean_statement"],
            "minimum_conditions": [f"Pre-registered study over {p_in['time_horizon']}"],
            "competing_hypotheses": [
                {"description": p_in["clean_statement"], "predicted_observations": [f"Improved outcome over {p_in['time_horizon']}"]},
                {"description": "Null or subgroup-restricted clinical effect", "predicted_observations": ["Null effect in unselected cohort"]},
            ],
        }
        lexical_scores.append(score_prediction(lex_pred, ref))

        # Baseline 3: Same-Category Nearest-Question-Type Neighbor Retrieval Baseline
        neighbor = benchmark_rows[(idx + 1) % len(benchmark_rows)]["reference_target"]
        for cand_idx, cand_row in enumerate(benchmark_rows):
            if cand_idx != idx and cand_row["category_id"] == row["category_id"] and cand_row["question_type"] == row["question_type"]:
                neighbor = cand_row["reference_target"]
                break
        retrieval_scores.append(score_prediction(neighbor, ref))

    def avg_metrics(score_list: list[dict]) -> dict:
        keys = ["study_mode_f1", "evidence_gap_f1", "pico_estimand_score", "hypothesis_coverage_score", "overall_composite_score"]
        return {k: round(sum(s[k] for s in score_list) / len(score_list), 2) for k in keys}

    summary = {
        "benchmark_name": "UnsolvedMedical-Bench v3.0",
        "version": "3.0.0",
        "total_problems": len(benchmark_rows),
        "categories_covered": 20,
        "problems_per_category": 25,
        "question_types_covered": len(qtype_counter),
        "by_question_type": dict(qtype_counter),
        "by_difficulty": dict(Counter(r["research_difficulty"] for r in benchmark_rows)),
        "evaluation_metrics": {
            "study_mode_f1": "Set F1 (0-100) on selecting the acceptable settling study designs (weight: 30%)",
            "evidence_gap_f1": "Set F1 (0-100) on diagnosing the underlying methodological evidence gaps (weight: 25%)",
            "pico_estimand_score": "Token F1 (0-100) on specifying target intervention, comparator, critical endpoint, and bias controls (weight: 25%)",
            "hypothesis_coverage_score": "Coverage score (0-100) on formulating falsifiable positive and null/subgroup competing hypotheses (weight: 20%)",
            "overall_composite_score": "Weighted composite score (0-100) across all 4 medical research dimensions",
        },
        "reference_baselines": {
            "uninformed_rct_majority_baseline": avg_metrics(majority_scores),
            "lexical_clinical_heuristic_baseline": avg_metrics(lexical_scores),
            "domain_neighbor_retrieval_baseline": avg_metrics(retrieval_scores),
            "oracle_reference_upper_bound": {
                "study_mode_f1": 100.0,
                "evidence_gap_f1": 100.0,
                "pico_estimand_score": 100.0,
                "hypothesis_coverage_score": 100.0,
                "overall_composite_score": 100.0,
            },
        },
    }
    SUMMARY_PATH.write_text(json.dumps(summary, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
    print(f"Built {len(benchmark_rows)} stratified benchmark problems in {BENCHMARK_PATH} and summary in {SUMMARY_PATH}.")
    print("Reference Baseline Composite Scores:")
    for name, m in summary["reference_baselines"].items():
        print(f"  - {name:38s}: {m['overall_composite_score']:6.2f} (StudyMode F1: {m['study_mode_f1']:5.2f}, Gap F1: {m['evidence_gap_f1']:5.2f}, PICO: {m['pico_estimand_score']:5.2f}, Hyp: {m['hypothesis_coverage_score']:5.2f})")


def evaluate_predictions_file(pred_path: Path) -> None:
    bench_rows = [json.loads(line) for line in BENCHMARK_PATH.read_text(encoding="utf-8").splitlines() if line.strip()]
    preds_by_id = {}
    for line in pred_path.read_text(encoding="utf-8").splitlines():
        if line.strip():
            obj = json.loads(line)
            key = obj.get("benchmark_id") or obj.get("problem_id") or obj.get("id")
            if key:
                preds_by_id[key] = obj

    scores = []
    for row in bench_rows:
        pred = preds_by_id.get(row["benchmark_id"]) or preds_by_id.get(row["problem_id"]) or {}
        scores.append(score_prediction(pred, row["reference_target"]))

    keys = ["study_mode_f1", "evidence_gap_f1", "pico_estimand_score", "hypothesis_coverage_score", "overall_composite_score"]
    avg = {k: round(sum(s[k] for s in scores) / len(scores), 2) for k in keys}
    print(json.dumps({"evaluated_problems": len(scores), "metrics": avg}, indent=2))


def main() -> None:
    parser = argparse.ArgumentParser(description="UnsolvedMedical v3.0 AI Benchmark Builder & Evaluator")
    parser.add_argument("--build", action="store_true", help="Build eval/benchmark_v3.jsonl and reference baselines")
    parser.add_argument("--predictions", type=Path, help="Path to model predictions JSONL file to score")
    args = parser.parse_args()

    if args.build or not BENCHMARK_PATH.exists():
        build_benchmark()
    if args.predictions:
        evaluate_predictions_file(args.predictions)


if __name__ == "__main__":
    main()

	#!/usr/bin/env python3
	"""
	UnsolvedMedical v3.0.0 — Frontier AI Study-Design & Hypothesis Benchmark Suite (`eval/`).

	Modes:
	1. `python3 scripts/evaluate_ai_benchmark.py --build`
	Extracts the stratified 500-problem Frontier Benchmark (`eval/benchmark_v3.jsonl`,
	25 problems per medical category x 20 categories, balanced across all 20 question types)
	and computes baseline reference scores in `eval/benchmark_summary.json`.
	2. `python3 scripts/evaluate_ai_benchmark.py --predictions <model_outputs.jsonl>`
	Evaluates an AI model's study-design, evidence-gap, PICO-estimand, and hypothesis predictions
	against `eval/benchmark_v3.jsonl`.
	"""

	import argparse
	import json
	import re
	from collections import Counter, defaultdict
	from pathlib import Path

	ROOT = Path(__file__).resolve().parent.parent
	BENCHMARK_PATH = ROOT / "eval" / "benchmark_v3.jsonl"
	SUMMARY_PATH = ROOT / "eval" / "benchmark_summary.json"


	def tokenize(text: str) -> set[str]:
	return {w.lower() for w in re.findall(r"[A-Za-z0-9]{3,}", text or "")}


	def set_f1(pred_set: set, gold_set: set) -> float:
	if not pred_set and not gold_set:
	return 1.0
	if not pred_set or not gold_set:
	return 0.0
	inter = len(pred_set & gold_set)
	if inter == 0:
	return 0.0
	prec = inter / len(pred_set)
	rec = inter / len(gold_set)
	return 2 * prec * rec / (prec + rec)


	def score_prediction(pred: dict, ref: dict) -> dict[str, float]:
	"""Score a single model prediction dictionary against a benchmark reference_target."""
	# 1. Study Mode Selection F1
	pred_modes = set(pred.get("acceptable_study_modes") or [])
	gold_modes = set(ref.get("acceptable_study_modes") or [])
	study_mode_f1 = set_f1(pred_modes, gold_modes) * 100.0

	# 2. Evidence Gap Diagnosis F1
	pred_gaps = set(pred.get("evidence_gap_types") or [])
	gold_gaps = set(ref.get("evidence_gap_types") or [])
	evidence_gap_f1 = set_f1(pred_gaps, gold_gaps) * 100.0

	# 3. PICO & Estimand Specification Score
	gold_pico_tokens = tokenize(
	f"{ref.get('target_intervention_or_predictor', '')} {ref.get('target_comparator', '')} "
	f"{ref.get('critical_outcome', '')} {' '.join(ref.get('minimum_conditions', []))}"
	)
	pred_pico_tokens = tokenize(
	f"{pred.get('target_intervention_or_predictor', '')} {pred.get('target_comparator', '')} "
	f"{pred.get('critical_outcome', '')} {pred.get('settlement_design_summary', '')} "
	f"{' '.join(pred.get('minimum_conditions', []))}"
	)
	pico_estimand_score = set_f1(pred_pico_tokens, gold_pico_tokens) * 100.0

	# 4. Falsifiable Competing Hypotheses Coverage Score
	gold_hyp_tokens = tokenize(
	" ".join(
	f"{h.get('description', '')} {' '.join(h.get('predicted_observations', []))}"
	for h in (ref.get("competing_hypotheses") or [])
	)
	)
	pred_hyps = pred.get("competing_hypotheses") or []
	pred_hyp_tokens = tokenize(
	" ".join(
	(f"{h.get('description', '')} {' '.join(h.get('predicted_observations', []))}" if isinstance(h, dict) else str(h))
	for h in pred_hyps
	)
	)
	has_two_hyps = 1.0 if len(pred_hyps) >= 2 else (0.5 if len(pred_hyps) == 1 else 0.0)
	hypothesis_score = (0.75 * set_f1(pred_hyp_tokens, gold_hyp_tokens) + 0.25 * has_two_hyps) * 100.0

	composite = (
	0.30 * study_mode_f1
	+ 0.25 * evidence_gap_f1
	+ 0.25 * pico_estimand_score
	+ 0.20 * hypothesis_score
	)
	return {
	"study_mode_f1": round(study_mode_f1, 2),
	"evidence_gap_f1": round(evidence_gap_f1, 2),
	"pico_estimand_score": round(pico_estimand_score, 2),
	"hypothesis_coverage_score": round(hypothesis_score, 2),
	"overall_composite_score": round(composite, 2),
	}


	def build_benchmark() -> None:
	problems = json.loads((ROOT / "problems.json").read_text(encoding="utf-8"))
	by_cat = defaultdict(list)
	for p in problems:
	if p.get("is_gold_seed"):
	by_cat[p["category_num"]].append(p)

	benchmark_rows = []
	qtype_counter = Counter()
	for cat_num in range(1, 21):
	cands = by_cat[cat_num]
	# Select 25 problems per category balancing question types and difficulty
	cands_sorted = sorted(
	cands,
	key=lambda r: (qtype_counter[r["question_type"]], -r["difficulty_level_id"], r["problem_number"]),
	)
	chosen = []
	used_ids = set()
	for _ in range(25):
	best = min(
	(c for c in cands if c["id"] not in used_ids),
	key=lambda r: (qtype_counter[r["question_type"]], -r["difficulty_level_id"], r["problem_number"]),
	)
	chosen.append(best)
	used_ids.add(best["id"])
	qtype_counter[best["question_type"]] += 1

	chosen.sort(key=lambda r: r["problem_number"])
	for r in chosen:
	benchmark_rows.append({
	"benchmark_id": f"UMED-BENCH-{len(benchmark_rows) + 1:04d}",
	"problem_id": r["id"],
	"category_id": r["category_id"],
	"question_type": r["question_type"],
	"research_difficulty": r["research_difficulty"],
	"prompt_input": {
	"title": r["title"],
	"statement": r["statement"],
	"clean_statement": r["clean_statement"],
	"background": r["background"],
	"condition": r["condition_or_health_state"]["name"],
	"population": r["population"]["description"],
	"time_horizon": r["time_horizon"],
	"known_partial_results": [k["summary"] for k in r["known_partial_results"]],
	"source_citation": r["source_citation"],
	},
	"reference_target": {
	"question_framework_type": r["question_framework"]["type"],
	"target_intervention_or_predictor": r["intervention_exposure_test_or_predictor"]["name"],
	"target_kind": r["intervention_exposure_test_or_predictor"]["kind"],
	"target_comparator": r["comparator_or_reference"],
	"critical_outcome": r["outcomes"][0]["name"],
	"safety_outcome": r["outcomes"][1]["name"] if len(r["outcomes"]) > 1 else "",
	"evidence_gap_types": r["evidence_gap_types"],
	"acceptable_study_modes": r["what_would_settle_it"]["acceptable_study_modes"],
	"settlement_design_summary": r["what_would_settle_it"]["summary"],
	"minimum_conditions": r["what_would_settle_it"]["minimum_conditions"],
	"competing_hypotheses": r["competing_hypotheses_or_interpretations"],
	"active_clinical_trial": r["status_assessment"]["active_research"][0] if r["status_assessment"]["active_research"] else "",
	},
	})

	BENCHMARK_PATH.parent.mkdir(parents=True, exist_ok=True)
	with BENCHMARK_PATH.open("w", encoding="utf-8") as f:
	for row in benchmark_rows:
	f.write(json.dumps(row, ensure_ascii=False, separators=(",", ":")) + "\n")

	# Compute 3 reference baselines on the 500 benchmark tasks:
	# 1. Majority-Class / Uninformed Clinical Baseline (always predicts standard RCT + general gap)
	# 2. Lexical / Prompt-Extraction Heuristic Baseline (infers study mode & PICO from prompt text keywords)
	# 3. Domain-Stratified Retrieval Baseline (retrieves nearest neighbor in same category)
	majority_scores = []
	lexical_scores = []
	retrieval_scores = []

	for idx, row in enumerate(benchmark_rows):
	ref = row["reference_target"]
	p_in = row["prompt_input"]
	stmt_low = p_in["statement"].lower()

	# Baseline 1: Majority-Class RCT Baseline
	maj_pred = {
	"acceptable_study_modes": ["randomized_trial", "prospective_cohort"],
	"evidence_gap_types": ["no_adequately_powered_study", "uncertain_long_term_effects"],
	"target_intervention_or_predictor": p_in["condition"],
	"target_comparator": "Standard of care",
	"critical_outcome": "Overall clinical survival and morbidity",
	"minimum_conditions": [f"Follow-up over {p_in['time_horizon']}"],
	"competing_hypotheses": [
	{"description": f"Intervention improves outcomes over {p_in['time_horizon']}", "predicted_observations": ["Superior primary outcome"]},
	],
	}
	majority_scores.append(score_prediction(maj_pred, ref))

	# Baseline 2: Lexical Clinical Keyword Heuristic Baseline
	inferred_modes = ["prospective_cohort"]
	if any(w in stmt_low for w in ("diagnostic", "accuracy", "sensitivity", "screening", "positivity")):
	inferred_modes = ["diagnostic_accuracy_study", "clinical_utility_study"]
	elif any(w in stmt_low for w in ("prognostic", "stratify", "risk prediction", "calibration")):
	inferred_modes = ["prognostic_validation", "prospective_cohort"]
	elif any(w in stmt_low for w in ("pathway", "molecular", "cellular", "mechanistic")):
	inferred_modes = ["mechanistic_human_study", "prospective_cohort"]
	elif any(w in stmt_low for w in ("implementation", "health system", "population", "disparities", "scaling")):
	inferred_modes = ["pragmatic_trial", "quasi_experimental_study"]
	else:
	inferred_modes = ["randomized_trial", "pragmatic_trial"]

	inferred_gaps = ["no_adequately_powered_study", "uncertain_generalizability"]
	if "diagnostic" in stmt_low or "measurement" in stmt_low:
	inferred_gaps = ["measurement_invalidity", "uncertain_reference_standard"]
	elif "prognos" in stmt_low or "classification" in stmt_low:
	inferred_gaps = ["model_not_externally_validated", "conflicting_interpretations"]

	lex_pred = {
	"acceptable_study_modes": inferred_modes,
	"evidence_gap_types": inferred_gaps,
	"target_intervention_or_predictor": p_in["clean_statement"],
	"target_comparator": "Standard clinical care",
	"critical_outcome": p_in["clean_statement"],
	"minimum_conditions": [f"Pre-registered study over {p_in['time_horizon']}"],
	"competing_hypotheses": [
	{"description": p_in["clean_statement"], "predicted_observations": [f"Improved outcome over {p_in['time_horizon']}"]},
	{"description": "Null or subgroup-restricted clinical effect", "predicted_observations": ["Null effect in unselected cohort"]},
	],
	}
	lexical_scores.append(score_prediction(lex_pred, ref))

	# Baseline 3: Same-Category Nearest-Question-Type Neighbor Retrieval Baseline
	neighbor = benchmark_rows[(idx + 1) % len(benchmark_rows)]["reference_target"]
	for cand_idx, cand_row in enumerate(benchmark_rows):
	if cand_idx != idx and cand_row["category_id"] == row["category_id"] and cand_row["question_type"] == row["question_type"]:
	neighbor = cand_row["reference_target"]
	break
	retrieval_scores.append(score_prediction(neighbor, ref))

	def avg_metrics(score_list: list[dict]) -> dict:
	keys = ["study_mode_f1", "evidence_gap_f1", "pico_estimand_score", "hypothesis_coverage_score", "overall_composite_score"]
	return {k: round(sum(s[k] for s in score_list) / len(score_list), 2) for k in keys}

	summary = {
	"benchmark_name": "UnsolvedMedical-Bench v3.0",
	"version": "3.0.0",
	"total_problems": len(benchmark_rows),
	"categories_covered": 20,
	"problems_per_category": 25,
	"question_types_covered": len(qtype_counter),
	"by_question_type": dict(qtype_counter),
	"by_difficulty": dict(Counter(r["research_difficulty"] for r in benchmark_rows)),
	"evaluation_metrics": {
	"study_mode_f1": "Set F1 (0-100) on selecting the acceptable settling study designs (weight: 30%)",
	"evidence_gap_f1": "Set F1 (0-100) on diagnosing the underlying methodological evidence gaps (weight: 25%)",
	"pico_estimand_score": "Token F1 (0-100) on specifying target intervention, comparator, critical endpoint, and bias controls (weight: 25%)",
	"hypothesis_coverage_score": "Coverage score (0-100) on formulating falsifiable positive and null/subgroup competing hypotheses (weight: 20%)",
	"overall_composite_score": "Weighted composite score (0-100) across all 4 medical research dimensions",
	},
	"reference_baselines": {
	"uninformed_rct_majority_baseline": avg_metrics(majority_scores),
	"lexical_clinical_heuristic_baseline": avg_metrics(lexical_scores),
	"domain_neighbor_retrieval_baseline": avg_metrics(retrieval_scores),
	"oracle_reference_upper_bound": {
	"study_mode_f1": 100.0,
	"evidence_gap_f1": 100.0,
	"pico_estimand_score": 100.0,
	"hypothesis_coverage_score": 100.0,
	"overall_composite_score": 100.0,
	},
	},
	}
	SUMMARY_PATH.write_text(json.dumps(summary, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
	print(f"Built {len(benchmark_rows)} stratified benchmark problems in {BENCHMARK_PATH} and summary in {SUMMARY_PATH}.")
	print("Reference Baseline Composite Scores:")
	for name, m in summary["reference_baselines"].items():
	print(f" - {name:38s}: {m['overall_composite_score']:6.2f} (StudyMode F1: {m['study_mode_f1']:5.2f}, Gap F1: {m['evidence_gap_f1']:5.2f}, PICO: {m['pico_estimand_score']:5.2f}, Hyp: {m['hypothesis_coverage_score']:5.2f})")


	def evaluate_predictions_file(pred_path: Path) -> None:
	bench_rows = [json.loads(line) for line in BENCHMARK_PATH.read_text(encoding="utf-8").splitlines() if line.strip()]
	preds_by_id = {}
	for line in pred_path.read_text(encoding="utf-8").splitlines():
	if line.strip():
	obj = json.loads(line)
	key = obj.get("benchmark_id") or obj.get("problem_id") or obj.get("id")
	if key:
	preds_by_id[key] = obj

	scores = []
	for row in bench_rows:
	pred = preds_by_id.get(row["benchmark_id"]) or preds_by_id.get(row["problem_id"]) or {}
	scores.append(score_prediction(pred, row["reference_target"]))

	keys = ["study_mode_f1", "evidence_gap_f1", "pico_estimand_score", "hypothesis_coverage_score", "overall_composite_score"]
	avg = {k: round(sum(s[k] for s in scores) / len(scores), 2) for k in keys}
	print(json.dumps({"evaluated_problems": len(scores), "metrics": avg}, indent=2))


	def main() -> None:
	parser = argparse.ArgumentParser(description="UnsolvedMedical v3.0 AI Benchmark Builder & Evaluator")
	parser.add_argument("--build", action="store_true", help="Build eval/benchmark_v3.jsonl and reference baselines")
	parser.add_argument("--predictions", type=Path, help="Path to model predictions JSONL file to score")
	args = parser.parse_args()

	if args.build or not BENCHMARK_PATH.exists():
	build_benchmark()
	if args.predictions:
	evaluate_predictions_file(args.predictions)


	if __name__ == "__main__":
	main()