Quality assessment using concordance and discordance rates in medical findings
Bibliographic record
Abstract
Purpose The purpose of this study was to determine the rate of concordance and discordance between clinical diagnosis and post‐mortem findings in patients admitted to the hospitals of the Saskatoon Health Region. Design/methodology/approach A retrospective record review of the medical and autopsy charts was carried out for all the deceased adult in‐patients admitted during calendar years 2002, 2003 and 2004. A total of 3416 in‐patient deaths were registered during the study period. Autopsies were performed on 206 of the deceased resulting in an autopsy rate of 6 percent. In accordance with selection criteria, 158 cases were included for this study. The mean age of subjects was 66.6±15.3 years with a range of 16‐94 years. The study group consisted of 92 males (58.2 percent) and 66 females (41.8 percent) with an average length of stay at the hospital of 12.9±10.9 days. Findings The concordance rate between clinical and autopsy diagnosis was found to be 75.3 percent. The discordance rate was 20.9 percent and in 3.8 percent of the study population a conclusive clinical or autopsy diagnosis was not finalized. Practical implications These results suggest that despite of the technical advances in medical and diagnostic modalities, diagnostic discrepancies in the present day health care system remain prevalent. Originality/value The authors encourage residents and physicians to continue using autopsy as an important tool to extend understanding of disease processes.
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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 | 0.107 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".