Bibliographic record
Abstract
Any autopsy has safety and risk management issues, which can arise in the preautopsy, autopsy, and postautopsy phases. The London Health Sciences Department of Pathology and Laboratory Medicine Autopsy Checklist was developed to address these issues. The current study assessed 1 measure of autopsy safety: the effectiveness of the checklist in documenting pathologists' communication of the actual or potential risk of blood-borne infections to support staff. Autopsy checklists for cases done in 2012 and 2013 were reviewed. The frequency of communication, as recorded in checklists, by pathologists to staff of previously diagnosed blood-borne infections (hepatitis B/C and human immunodeficiency virus) or the risk of infection based on lifestyle (eg, intravenous drug abuse) was tabulated. These data were compared with medical histories of the deceased and circumstances of their deaths described in the final autopsy reports. Information about blood-borne infections was recorded less frequently in the checklists compared with the final reports. Of 4 known human immunodeficiency virus cases, there was no checklist documentation in 3. All 11 hand injuries were documented. None of these cases had known infectious risks. The Autopsy Checklist is a standardized means of documenting safety and risk issues arising during the autopsy process, but its effectiveness relies on accurate completion.
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.
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 | 0.017 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.021 |
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".