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Life sciences · Preprint
arXiv · September 10, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is a preprint proposing a distribution-free statistical certification framework to attach finite-sample validity guarantees to crash-severity prediction models, addressing label noise and deployment shift. The framework is applied to 5.2 million historical crash records across multiple models and jurisdictions, but has not undergone peer review and does not report clinical validation, impact on dispatch or screening decisions, or prospective performance.
Retrospective registry analysis with methodological framework demonstration. Crash records from Texas, with detailed analysis of vulnerable road users subgroup. Inclusion criteria for crash records not specified.. Intervention: Certification layer providing distribution-free validity guarantees and prediction set bounds for any crash-severity model.. Compared with: Null; framework applied to seven existing base models without direct model-to-model comparison reported.. n = 5,200,000. Texas.
On 5.2 million Texas records, the framework certifies a model-independent floor on prediction set width for vulnerable road users that no base model beats across four decades. Recorded KABCO label agrees with medical severity only about 50% of the time, motivating the structured misreporting model. Seven base models spanning four decades analyzed; framework shown to attach identical validity across all.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
If adopted, the framework could improve trust in deployed severity prediction models by providing statisticallybacked bounds on what each prediction means. However, without evidence of prospective clinical utility, decision-impact, or peer-reviewed validation, clinicians and dispatchers should not rely on this framework in practice until those gaps are filled.
A methodological framework paper presenting an algorithmic certification layer for crash-severity prediction models, demonstrated on a large registry but without clinical validation, peer review, or evidence that the method changes model performance or clinical decision-making.
As stated by the source record.
Quoted from the source exactly as published.
If adopted, the framework could improve trust in deployed severity prediction models by providing statisticallybacked bounds on what each prediction means. However, without evidence of prospective clinical utility, decision-impact, or peer-reviewed validation, clinicians and dispatchers should not rely on this framework in practice until those gaps are filled.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Crash-severity models inform screening, dispatch and site prioritization, yet are deployed without a finite-sample statement of what one prediction means. Off-the-shelf guarantees fail here, because the features that make crash severity distinctive defeat them: the KABCO outcome is ordinal, the recorded label is a field assessment agreeing with medical severity about half the time, erring in a structured way, and deployment crosses jurisdictions and years calibration never saw. We develop a certification layer that wraps any severity model unmodified, with distribution-free guarantees using this structure: contiguous ordinal sets that read as "B or worse"; per-class validity for any pre-declared partition, with an oracle efficiency characterization; transfer of coverage to unobserved true severity through a declared reporting band, with a worst-case sharpness result; a one-sided certificate under deployment shift; and severity-weighted risk control. The guarantees compose with an attributable slack budget. The same analysis bounds what certification can achieve. A certified set's informativeness is governed by a functional of the true law that no base model can evade and that cannot be lower-bounded distribution-free; given a declared misreporting channel identified from record-linkage data, a nonvacuous lower bound on that floor becomes computable. On 5.2 million Texas records across seven base models spanning four decades, the layer attaches identical validity and certifies, on the vulnerable road users, a model-independent floor on set width that no base model beats, separating it from a remainder that stays bounded but distribution-free unidentifiable. The framework is released as an open-source package with theorem-level tests.
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