The Role of Autopsy in Quality Assurance
Bibliographic record
Abstract
ABSTRACT: Hospital autopsies frequently reveal errors in diagnosis that could have affected the patient's clinical outcome. The aims of this study were (1) to investigate the ability of autopsy at our institution to elucidate unrecognized antemortem diagnoses and (2) to pilot a method for tabulating diagnostic discrepancies on a prospective basis. The study sample consisted of 296 cases from our hybrid hospital/forensic autopsy service during the period 2016 to 2018. Discrepancies in autopsy and clinical diagnosis were reported by pathologists at the time of autopsy report generation using a standard form. The rates of major discrepancies between autopsy and clinical diagnoses were 37.5% for in-hospital cases and 25% for patients who died outside our hospital ( P < 0.05). The most common discrepant category was infection. The overall rates of discrepant causes of death were 14% (in hospital) and 8% (out of hospital) (ns). Overall percentages of cases with major diagnostic discrepancies were higher in our study than have been previously reported. It is possible that the nature of our patient population plays a role in this result. This study describes an important prospective reporting tool that will allow us to track rates of medical errors and improve diagnosis and treatment of the critically ill.
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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 | 0.172 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".