{
  "pdf": "livemedbench-contextual-constraints-guardrail.pdf",
  "title": "EVIDENCE-GROUNDED CONSTRAINT SCHEMAS DO NOT IMPROVE MEDICAL LLM GUARDRAILS LIVEMEDBENCH FARS",
  "elapsed": 41.3,
  "runs_mode": 1,
  "valid_runs": 1,
  "avg_score": 3.5,
  "scores": [
    3.5
  ],
  "score_std": 0,
  "final_verdict": "Strong Reject",
  "final_confidence": 0.6,
  "conference_scores": {
    "soundness": 2.3,
    "presentation": 2.8,
    "contribution": 2,
    "overall_rating": 3.5,
    "confidence": 3
  },
  "strengths": [
    "Clear negative result with strong internal consistency: all 9 experimental conditions (3 main + 6 optimization variants across 3 pipeline architectures) consistently fail to improve over the single-pass baseline (Tables 1–2), which substantially strengthens confidence in the negative finding.",
    "Well-controlled experimental design: Conditions B and C share identical information (constraint content + evidence quotes) and differ only in representation format (plain-text checklist vs. structured JSON schema), cleanly isolating the effect of schema structure (Section 2.2, Figure 1).",
    "Insightful failure analysis identifying 'cautious bias' mechanism: the paper quantifies the asymmetry—116 positive criteria lost vs. 55 negative criteria avoided—providing a concrete explanatory mechanism rather than merely reporting negative numbers (Section 3.3)."
  ],
  "weaknesses": [
    "Severe generalizability limitation: only one model (Qwen3-14B-Instruct) is tested on one benchmark (LiveMedBench v202601). The conclusion that 'single-pass prompts are more effective' cannot be extended to other model families, sizes, or medical domains. The paper itself acknowledges this in Section 5 but does nothing to mitigate it—even testing GPT-4 or a smaller Qwen variant would significantly strengthen the claim.",
    "No statistical significance testing: despite high within-condition variance (SD 0.29–0.43 reported in Section 3.3), no confidence intervals, p-values, or effect size measures are provided. With N=500 and SD~0.35, the observed deltas (−0.013, −0.024) are plausibly within noise. This is a critical omission for a paper whose core contribution is a negative empirical result.",
    "Evaluation relies entirely on an LLM-based grader (GPT-4.1) with no human validation: the constraint-focused rubric scores and positive/negative criteria counts are all computed by GPT-4.1 via LiveMedBench's automated script. No inter-annotator agreement, human calibration, or error analysis of the grader itself is provided, raising concerns about measurement reliability—especially for a paper whose deltas are small."
  ],
  "must_fix_items": [
    "Add statistical significance tests (e.g., paired bootstrap or Wilcoxon signed-rank on case-level scores) to determine whether the observed deltas are meaningful given the high variance; a negative result without significance testing is not actionable.",
    "Test at least one additional model (different family or scale) to assess whether 'cautious bias' is a general phenomenon or a Qwen3-14B artifact.",
    "Report inter-rater reliability or human calibration for the GPT-4.1 grader on at least a sample of cases; without this, the measurement validity of small deltas is questionable."
  ],
  "runs": [
    {
      "run": 1,
      "score": 3.5,
      "verdict": "Strong Reject",
      "confidence": 0.6,
      "strengths": [
        "Clear negative result with strong internal consistency: all 9 experimental conditions (3 main + 6 optimization variants across 3 pipeline architectures) consistently fail to improve over the single-pass baseline (Tables 1–2), which substantially strengthens confidence in the negative finding.",
        "Well-controlled experimental design: Conditions B and C share identical information (constraint content + evidence quotes) and differ only in representation format (plain-text checklist vs. structured JSON schema), cleanly isolating the effect of schema structure (Section 2.2, Figure 1).",
        "Insightful failure analysis identifying 'cautious bias' mechanism: the paper quantifies the asymmetry—116 positive criteria lost vs. 55 negative criteria avoided—providing a concrete explanatory mechanism rather than merely reporting negative numbers (Section 3.3)."
      ],
      "weaknesses": [
        "Severe generalizability limitation: only one model (Qwen3-14B-Instruct) is tested on one benchmark (LiveMedBench v202601). The conclusion that 'single-pass prompts are more effective' cannot be extended to other model families, sizes, or medical domains. The paper itself acknowledges this in Section 5 but does nothing to mitigate it—even testing GPT-4 or a smaller Qwen variant would significantly strengthen the claim.",
        "No statistical significance testing: despite high within-condition variance (SD 0.29–0.43 reported in Section 3.3), no confidence intervals, p-values, or effect size measures are provided. With N=500 and SD~0.35, the observed deltas (−0.013, −0.024) are plausibly within noise. This is a critical omission for a paper whose core contribution is a negative empirical result.",
        "Evaluation relies entirely on an LLM-based grader (GPT-4.1) with no human validation: the constraint-focused rubric scores and positive/negative criteria counts are all computed by GPT-4.1 via LiveMedBench's automated script. No inter-annotator agreement, human calibration, or error analysis of the grader itself is provided, raising concerns about measurement reliability—especially for a paper whose deltas are small."
      ],
      "must_fix_items": [
        "Add statistical significance tests (e.g., paired bootstrap or Wilcoxon signed-rank on case-level scores) to determine whether the observed deltas are meaningful given the high variance; a negative result without significance testing is not actionable.",
        "Test at least one additional model (different family or scale) to assess whether 'cautious bias' is a general phenomenon or a Qwen3-14B artifact.",
        "Report inter-rater reliability or human calibration for the GPT-4.1 grader on at least a sample of cases; without this, the measurement validity of small deltas is questionable."
      ],
      "conference_scores": {
        "soundness": 2.3,
        "presentation": 2.8,
        "contribution": 2,
        "overall_rating": 3.5,
        "confidence": 3
      }
    }
  ]
}