Bibliographic record
Abstract
ABSTRACT: Distinguishing artifactual postmortem hypostatic hemorrhages from injury is key to accurately diagnosing strangulation. Despite being a well-known phenomenon, the literature is limited. This retrospective case-control analysis characterizes anterior neck musculature hemorrhage patterns associated with postmortem artifact versus strangulation by comparing incidental neck hemorrhages noted on 20 autopsy reports from 2020 to 2021 to 10 strangulation controls from 2015 to 2021 in Northern Nevada. Cases were analyzed for body position and location/severity of musculature involvement. For artifact cases, 50.0% were prone, 40.0% supine, and 10.0% side-lying. A total of 55.6% of artifact cases and controls demonstrated neck hemorrhage laterality. A total of 80.0% of the prone cases versus 77.8% of supine had diffuse hemorrhage versus focal. A total of 63.2% of artifact cases involved the sternocleidomastoid versus 70.0% controls ( P = 1.000), 26.3% involved soft tissues versus 20.0% ( P = 1.000), 9.1% the sternohyoid versus 40.0% ( P = 0.149), 27.3% the sternothyroid versus 60.0% ( P = 0.198), 9.1% the thyrohyoid versus 10.0% ( P = 1.000), 18.2% the omohyoid versus 30.0% ( P = 0.635), and 10.0% the tongue versus 50.0% ( P = 0.026). Despite the limitations, this study demonstrated that while prone positioning is a contributing factor to the development of anterior neck hemorrhages, there are other factors than postmortem hypostasis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.087 | 0.031 |
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".