Bibliographic record
Abstract
A critical analysis of the postmortem diagnostic criteria for asphyxia leads to a difficulty that has significant implications for the forensic pathologist. The difficulty is that there are no universally recognized pathognomic signs of asphyxia, but pathologists frequently make this diagnosis based on observations that individually have indeterminate significance but combined together, in the appropriate context, have diagnostic value. This leads to the question: if asphyxia has no recognizable signs at autopsy how are we able to diagnose this entity in the latter circumstances? This problem can be solved by defining the minimally adequate diagnostic criteria for compressive neck injury. These criteria are potentially well-defined but currently lacking, in part, due to incomplete morphological characterization of the injuries that frequently occur in strangulation. The problem is especially challenging because the signs of strangulation form a spectrum of degree from minimal to marked and there is no consensus as to the minimal number and nature of lesions that is required to make the diagnosis of strangulation. In the present review of lesions that are commonly encountered in strangulation, intracartilaginous laryngeal haemorrhages and laryngeal cartilage microfractures are considered to have unrecognized diagnostic value, and these lesions are described in detail. A triad of haemorrhages (subepithelial laryngeal haemorrhage, intralaryngeal muscular haemorrhage, and intracartilaginous laryngeal haemorrhage) is discussed in the context of developing a definition of strangulation based on morphological criteria in the absence of overt mechanical injuries to the neck. Although definitive criteria for cases with minimal findings are still lacking, several lesions have putative diagnostic value.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".