Companion Cases in a Large Urban Medical Examiner's Office
Bibliographic record
Abstract
Companion death cases, as defined in this study, include 2 or more deaths that occur at the same location or 1 death at a specific location combined with 1 or more individuals transported from that same location to a hospital where death was pronounced within 1 hour of arrival. These types of cases can have multiple causes and manners of death. The Wayne County Medical Examiner's Office conducted a retrospective study of companion death cases that came into the office from mid 2007 to the end of 2014. The purpose of the study was to identify and examine patterns of companion death cases in a large urban area that would assist future companion death case investigations. Three hundred fifty deaths were found to be companion cases, including 135 pairs (2 connected deaths in the same location), 20 trios, and 5 quartets. Approximately 49% of companion case deaths were homicides. Approximately 30% of companion case deaths were traumatic accidental deaths. Around 14% of companion case deaths that were from the same scene location had different manners of death, including suicide, homicide, natural, and indeterminate. The remainder of companion death cases were either drug related or natural. Through this study, we have identified a pattern to these companion death cases and have concluded that it is important to conduct a thorough medicolegal death investigation of such cases to establish and elucidate the true circumstances surrounding these deaths.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".