Medicolegal Investigation for Cremation Clearance
Bibliographic record
Abstract
This study on cremation clearance examines whether physical inspections detect more unnatural unreported deaths than medicolegal investigations without inspections. We reviewed all deaths reported to the medical examiner for cremation clearance during 2 distinct years and compared subsequent amendments of death certificates after 2 different investigative methodologies (1 with and 1 without physical inspection). Of 10,367 deaths in 2012, there were 86 deaths (0.83%) in which the investigation with physical inspection resulted in amendments to the death certificate. Of 11,906 deaths in 2016 without physical inspection, there were 153 that resulted in amendments (1.3%) including 2 homicides. For the detection of accidents, there was no statistically significant difference (χ = 0.8119, P = 0.367552). For cremation investigations, the work effort and costs of performing physical inspections do not appear justified given the similar detection rates (approximately 1%) for unnatural deaths among the 2 groups. Both methods, however, do detect unreported unnatural deaths.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".