Histological Aging of Bruising: A Historical and Ongoing Challenge
Bibliographic record
Abstract
Dating of bruises can be of great importance in forensic pathology. Such dating can be performed by both naked eye appearance and by using microscopic techniques. This paper reviews the literature on histological dating of bruising. Microscopic techniques have used standard histologic stains including hematoxylin and eosin and Prussian blue for iron; more recently, studies have employed immunohistochemistry. Biochemical techniques have also been used in an attempt to date bruises. These data have provided estimation of the age of bruises, without being able to give precise determinations. Findings that have been used to age bruises and factors that affect the aging of bruises are reviewed. Dating of bruising by laboratory techniques can only provide a range of time. Biological variation may prevent more accurate dating, despite newer techniques being used. Histological examination of bruises does have added value, but must be interpreted appropriately.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".