Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Postmortem changes are well known for their possible misinterpretation as traumatic lesions which can mislead to suspicion of violent death and therefore to a forensic autopsy request. As far as we know, a systematic review of the prevalence of such a reason for coroner's autopsy request has not been done yet. A retrospective study of 230 forensic autopsies requested by the Coroner's office from 2002 to 2004 in the province of Quebec, Canada, was conducted by the authors. Of the 230 reviewed cases, postmortem artifacts mistaken for traumatic lesions were found in 18 cases. These misinterpretation were based on 5 categories of portmortem changes: purge fluid drainage in 12 cases (66.7%), bluish discoloration by lividity in 5 cases (27.8%), parchment-like drying of the skin in 4 cases (22.2%), bloating from gas formation in 4 cases (22.2%), and skin slippage in 1 case (5.56%). Therefore, postmortem artifacts misinterpretation occurred in 7.83% (95% confidence interval 0.05-0.12) of all requested forensic autopsies and in 35.29% (95% confidence interval 0.23-0.50) of decomposed autopsy cases. This study clearly establishes the high prevalence of postmortem artifacts as main reason for forensic autopsy request. Hence, in a context of forensic pathologist shortage, the improvement of coroner continuous training may reduce the workload.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it