What is a Complete Autopsy?
Bibliographic record
Abstract
Postmortem examinations have taken place over the past several thousand years. Despite this, the definition of a “complete” autopsy remains nebulus and the subject of controversy. Although ‘minimal autopsy practice standards’ have been published by professional bodies globally, recognition of, and adherence to those standards remains sporadic. An underlying refutation that ‘autopsies can never be complete’ – the reductio ad absurdum fallacy – has influenced many forensic pathologists’ opinions about autopsy. More pragmatic pathologists attempt to balance the financial and workload burdens of autopsies with the principles of adequacy and accuracy. Some medical examiners cite “statutory duty” as the force guiding the nature and completeness of their work, and as such, external examinations, partial autopsies and other limited variants are substituted for complete autopsies. Although it is impossible to perform every conceivable test in any one autopsy, an evidence-based approach guided by three forensic autopsy goals – statutory duty, the creation of a minimal dataset for societal and governmental inquiry, and maintenance of practitioner competency – ensure the completeness of any one postmortem examination.
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 | 0.069 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.010 | 0.025 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".