Bibliographic record
Abstract
Forensic pathologists have a duty to determine the cause and manner of death and are bound by international guidelines in the completion of the death certificate. Sometimes, there are complex circumstances surrounding a death that cannot be captured in the structure of the death certificate and its requirement of listing only 1 cause of death per line. Cases may have multiple causes of death with comorbid medical conditions or inflicted injuries that equally contribute to the ultimate demise. Compared with other forms of homicide, autopsy evidence of strangulation will often be found with other life-threatening traumatic injuries. The Wayne County Medical Examiner's Office conducted a retrospective study of strangulation cases that came into the office from mid-2007 to the end of 2016. The purpose of the study was to examine patterns of injuries in strangulation cases and identify those with additional traumatic injuries of commensurate extent that required incorporation into the cause of death. A total of 43 strangulation cases were found, of which there were equal numbers of ligature and manual strangulations (19 each) and 5 cases in which the method was not specified, and decedents were divided: 63% female and 37% male. Fourteen of these cases were recognized to have multiple causes of death, where blunt force trauma was the most common additional cause, and the sex distribution weighed heavily toward the female (approximately 79%).
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".