Interpretive Diagnostic Error Reduction in Surgical Pathology and Cytology: Guideline From the College of American Pathologists Pathology and Laboratory Quality Center and the Association of Directors of Anatomic and Surgical Pathology
Bibliographic record
Abstract
CONTEXT: Additional reviews of diagnostic surgical and cytology cases have been shown to detect diagnostic discrepancies. OBJECTIVE: To develop, through a systematic review of the literature, recommendations for the review of pathology cases to detect or prevent interpretive diagnostic errors. DESIGN: The College of American Pathologists Pathology and Laboratory Quality Center in association with the Association of Directors of Anatomic and Surgical Pathology convened an expert panel to develop an evidence-based guideline to help define the role of case reviews in surgical pathology and cytology. A literature search was conducted to gather data on the review of cases in surgical pathology and cytology. RESULTS: The panel drafted 5 recommendations, with strong agreement from open comment period participants ranging from 87% to 93%. The recommendations are: (1) anatomic pathologists should develop procedures for the review of selected pathology cases to detect disagreements and potential interpretive errors; (2) anatomic pathologists should perform case reviews in a timely manner to avoid having a negative impact on patient care; (3) anatomic pathologists should have documented case review procedures that are relevant to their practice setting; (4) anatomic pathologists should continuously monitor and document the results of case reviews; and (5) if pathology case reviews show poor agreement within a defined case type, anatomic pathologists should take steps to improve agreement. CONCLUSIONS: Evidence exists that case reviews detect errors; therefore, the expert panel recommends that anatomic pathologists develop procedures for the review of pathology cases to detect disagreements and potential interpretive errors, in order to improve the quality of patient care.
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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 | 0.170 | 0.308 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.017 | 0.007 |
| Research integrity | 0.018 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".