The role of autopsy on patients with burns
Bibliographic record
Abstract
Burn center verification requires the use of autopsy as one method of quality assurance in a burn center. Because of the decreasing rates of autopsies worldwide and improved diagnostic accuracy in our critical care units, we tested the hypothesis that autopsy diagnosis would not alter our clinical diagnosis. A chart review of all deaths (N = 94) that occurred during a 6-year period (1989-1994) was performed. The clinical diagnoses from the hospital charts and autopsy reports for the patients were reviewed, and diagnostic discrepancies were classified as class I or class II errors. Class I diagnostic errors might have altered the clinical outcome. Class II errors were attributable to the burn injuries but were believed to have had little impact on the clinical outcome. The overall autopsy rate was 93.6% (n = 88). Clinical diagnostic errors were found in 16 (18%) of 88 patients. Five class I errors were found in 4 patients (4.5%), and 15 class II errors were found in 13 patients (14.7%). Although the rate of potentially serious errors was low (only 4.5% of the patients in this study) postmortem examinations revealed clinical diagnostic errors. The results of this study support the continued use of autopsies as a means of quality assurance, despite our ability to closely monitor our critically ill patients with burns.
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".