Discrimination of Falls and Blows in Blunt Head Trauma: Assessment of Predictability Through Combined Criteria*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The discrimination of falls from homicidal blows in blunt head injuries is a common but difficult problem in both forensic anthropology and pathology. Three criteria have been previously proposed for this distinction: the hat brim line rule, side lateralization of fractures, and number of lacerations. The aim of the present study was to achieve a better distinction rate by combining those criteria and assess the predictability of these combined criteria tools. Over a 6-year period, a total of 114 cases (92 males and 22 females) were studied: 21 cases of downstairs falls, 29 cases of falls from one's own height, and 64 cases of head trauma by a blunt weapon. The results revealed predictability rates varying from 62.5 to 83.3% for criteria pointing towards a fall. As for combined criteria in favor of a blow, the assumption was accurate in all cases (100%).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it