{
  "scope": "Same-cleaned-input implementation cross-check; not independent sample replication, clinical validation, or raw-cleaning proof.",
  "scoring_version": "pdf-key-v2",
  "method": "Unweighted, unpenalized Bernoulli logistic maximum likelihood; intercept; predictors treated as supplied numeric values; Newton steps with backtracking; model-based inverse-information standard errors; two-sided normal Wald tests and 95% confidence intervals.",
  "outcome": "Q21=1 versus Q21=0",
  "predictors": [
    "Knowledge_Score_Total",
    "Q43",
    "Q44",
    "Q15"
  ],
  "csv_sha256": "501042c4be0c3c7f40eab1cc1907d0e23607b609212af8fbcd846209c88c7e37",
  "python": "3.14.3",
  "counts": {
    "total": 348,
    "complete_cases": 276,
    "excluded": 72,
    "missing_required_field": 72,
    "invalid_numeric_or_outcome": 0
  },
  "coefficients": [
    {
      "term": "Intercept",
      "coefficient": -4.997390253333899,
      "standard_error": 1.361883882030325,
      "odds_ratio": 0.006755554299105676,
      "coefficient_wald95ci": [
        -7.666633613238932,
        -2.3281468934288663
      ],
      "odds_ratio_wald95ci": [
        0.00046819128672388543,
        0.09747621363803763
      ],
      "wald_z": -3.6694686817819484,
      "p_two_sided": 0.00024305511100575715
    },
    {
      "term": "Knowledge_Score_Total",
      "coefficient": 0.13843326592348998,
      "standard_error": 0.08761888350211705,
      "odds_ratio": 1.148473036725293,
      "coefficient_wald95ci": [
        -0.033296590106270174,
        0.31016312195325013
      ],
      "odds_ratio_wald95ci": [
        0.9672516397757153,
        1.3636475368404255
      ],
      "wald_z": 1.5799478421811337,
      "p_two_sided": 0.11411881160895415
    },
    {
      "term": "Q43",
      "coefficient": 0.4539041013050319,
      "standard_error": 0.16602661527573204,
      "odds_ratio": 1.5744470028221724,
      "coefficient_wald95ci": [
        0.1284979148895095,
        0.7793102877205542
      ],
      "odds_ratio_wald95ci": [
        1.1371190502698827,
        2.1799681960366293
      ],
      "wald_z": 2.733923717900299,
      "p_two_sided": 0.006258454166003293
    },
    {
      "term": "Q44",
      "coefficient": 0.27627956797044034,
      "standard_error": 0.32651324152671907,
      "odds_ratio": 1.3182163435851397,
      "coefficient_wald95ci": [
        -0.36367462589735705,
        0.9162337618382377
      ],
      "odds_ratio_wald95ci": [
        0.6951173311293014,
        2.4998575789671107
      ],
      "wald_z": 0.846151190311931,
      "p_two_sided": 0.39746840681626505
    },
    {
      "term": "Q15",
      "coefficient": 0.8885778761735915,
      "standard_error": 0.3496194012670818,
      "odds_ratio": 2.431669056672647,
      "coefficient_wald95ci": [
        0.2033364413936538,
        1.5738193109535292
      ],
      "odds_ratio_wald95ci": [
        1.225484702678518,
        4.825041380161889
      ],
      "wald_z": 2.541557685166298,
      "p_two_sided": 0.011035973368686041
    }
  ],
  "diagnostics": {
    "converged": true,
    "iterations": 6,
    "log_likelihood": -144.7218224077344,
    "gradient_max_abs": 3.1530333899354446e-13,
    "history": [
      {
        "iteration": 1,
        "log_likelihood": -146.70836957485207,
        "step_max_abs": 3.450902586054589,
        "step_scale": 1.0
      },
      {
        "iteration": 2,
        "log_likelihood": -144.76628143105435,
        "step_max_abs": 1.3076995768076387,
        "step_scale": 1.0
      },
      {
        "iteration": 3,
        "log_likelihood": -144.72185824636873,
        "step_max_abs": 0.23240850134897106,
        "step_scale": 1.0
      },
      {
        "iteration": 4,
        "log_likelihood": -144.7218224077605,
        "step_max_abs": 0.006374537546187214,
        "step_scale": 1.0
      },
      {
        "iteration": 5,
        "log_likelihood": -144.7218224077344,
        "step_max_abs": 5.051573040396397e-06,
        "step_scale": 1.0
      },
      {
        "iteration": 6,
        "log_likelihood": -144.7218224077344,
        "step_max_abs": 3.4724855945762746e-12,
        "step_scale": 1.0
      }
    ],
    "alternate_start_max_abs_coefficient_difference": 9.769962616701378e-15,
    "finite_difference_gradient_max_abs_error": 8.640202819876207e-07,
    "finite_difference_information_max_abs_error": 7.698790795984678e-06
  },
  "limitations": [
    "Uses the existing cleaned input; does not verify raw recoding, consent, sampling, construct validity, or cleaning provenance.",
    "No survey weighting, clustering, robust variance, interactions, nonlinearity assessment, out-of-sample evaluation, or causal interpretation.",
    "Agreement would establish this model calculation only; it would not validate the full engine or every reported analysis."
  ],
  "verification": {
    "performed_on": "2026-09-10",
    "authorship": "Separate AI-agent logistic implementation, originally written without reading Sutrix engine code; rerun with an independently implemented PDF-key score correction.",
    "comparison": "All 25 version-2 beta, odds ratio, confidence-limit and p-value entries agree at six-decimal rounding.",
    "maximum_absolute_difference": 4.6315957447085054e-07,
    "synthetic_check": "Two-group binomial coefficients/covariance agree with closed form within 1e-10; PDF-key flips, each missing item, complete-item aggregation and invalid-item rejection pass.",
    "reproduce": "python3 concussion-model-check.py --csv /path/to/authorized-historical-cleaned-input.csv --pdf-key --output result.json",
    "scope": "Same cleaned sample; not independent scientific review or independent-sample replication.",
    "engine_model_input": {
      "sha256": "e94a8c5fcd8faf188de642e5d1199b71d63fdd0da2a25aa7de8ee3e3ab6c1239",
      "role": "Separately generated PDF-key-v2 derivative used by Sutrix engine. Its fingerprint differs from csv_sha256, which identifies this checker\u2019s historical input before in-memory correction."
    }
  },
  "input_role": "Historical cleaned CSV; Q31/Q37 correction and 19-item sum are applied in memory before fitting. No derivative CSV is emitted."
}