Patterns of Error in Interpretive Pathology
Bibliographic record
Abstract
OBJECTIVES: To demonstrate how the educational presentation and targeted review of cases with discrepant interpretive findings between pathologists can raise awareness for specific diagnostic errors through identification of common overarching patterns of error in interpretive pathology. METHODS: We performed a review of 147 surgical pathology and cytopathology cases of discordances from 23 PowerPoint presentations presented between 2010 and 2017. Pathologists and pathology residents, blinded from the official interpretations, were presented each case and surveyed for their own diagnostic assessments. Survey results were compared with the final/correct interpretations of the signing pathologists. RESULTS: Of the 134 cases with available survey results, there were 87 (64.9%) for which most survey respondents proposed a diagnostic interpretation concordant with the final/correct diagnosis. There were 37 (27.6%) cases for which most survey responses were either wholly or partially discordant with the final/correct diagnosis. For 10 (7.5%) cases, there were equal numbers of concordant and discordant survey responses. CONCLUSIONS: Our analyses of the cases with frequent erroneous diagnoses reveal common patterns of error that are widely applicable and outline specific error-prone interpretive tendencies. Greater awareness for these tendencies, highlighted by presentation of discordant cases, can improve the quality of diagnostic pathology services.
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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 | 0.030 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".