Classification of Asphyxia: The Need for Standardization
Bibliographic record
Abstract
The classification of asphyxia and the definitions of subtypes are far from being uniform, varying widely from one textbook to another and from one paper to the next. Unfortunately, similar research designs can lead to totally different results depending on the definitions used. Closely comparable cases are called differently by equally competent forensic pathologists. This study highlights the discrepancies between authors and tries to draw mainstream definitions, to propose a unified system of classification. It is proposed to classify asphyxia in forensic context in four main categories: suffocation, strangulation, mechanical asphyxia, and drowning. Suffocation subdivides in smothering, choking, and confined spaces/entrapment/vitiated atmosphere. Strangulation includes three separate forms: ligature strangulation, hanging, and manual strangulation. As for mechanical asphyxia, it encompasses positional asphyxia as well as traumatic asphyxia. The rationales behind this proposed unified model are discussed.
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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 | 0.147 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.012 | 0.008 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".