Radiographs Interpretation by Forensic Pathologists
Bibliographic record
Abstract
In child deaths investigation, radiologic examination is particularly important in the diagnosis of child abuse. In the province of Quebec, Canada, all autopsies for suspicious deaths are performed at a centralized forensic laboratory where, because of budget restrictions, forensic pathologists rely on their own knowledge for radiographs interpretations. To assess the validity of this radiologic examination by nonradiologist forensic specialist, we reviewed all cases of child death on a 1-year period. A total of 20 cases were reviewed by an experienced pediatric radiologist, and this interpretation was compared with pathologist's conclusions. Forensic pathologists missed an important finding in 3 positive cases. Yet, none of those missed findings would have significantly changed the cases outcome, because other autopsy findings had already oriented the final diagnosis. Nevertheless, this result is alarming. In a general context of financing problems, it can be appealing to management team to restrict access to external consultants. This study is important in reminding that such money savings do not come without a decrease in quality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".