Autopsies and death certification in deaths due to blunt trauma: what are we missing?
Bibliographic record
Abstract
OBJECTIVES: To determine the frequency, body region and severity of injuries missed by the clinical team in patients who die of blunt trauma, and to examine the accuracy of the cause of death as recorded on death certificates. DESIGN: A retrospective review. SETTING: London Health Sciences Centre, London, Ont. PATIENTS: One hundred and eight deaths due to blunt trauma occurring during the period Apr. 1, 1991, to Mar. 31, 1997. Two groups were considered: clinically significant missed injuries were identified by comparing patient charts only (group 1) and more detailed injury lists from the autopsies and charts of the patients (group 2). OUTCOME MEASURES: Chart and autopsy findings. RESULTS: Of the 108 patients, 78 (72%) were male, and they had a median age of 39 years (range from 2 to 90 years). The most common cause of death was neurologic injury (27%), followed by sepsis (17%) and hemorrhage (15%). There was disagreement between the treating physicians and the causes of death listed on the death certificate in 40% of cases and with the coroner in 7% of cases. Seventy-seven clinically significant injuries were missed in 51 (47%) of the 108 patient deaths. Injuries were missed in 29% of inhospital deaths and 100% of emergency department deaths. Abdominal and head injuries accounted for 43% and 34% of the missed injuries, respectively. CONCLUSIONS: The information contained on the death certificate can be misleading. Health care planners utilizing this data may draw inaccurate conclusions regarding causes of death, which may have an impact on trauma system development. Missed injuries continue to be a concern in the management of patients with major blunt trauma.
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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 | 0.023 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".