Interpretation of Skin Biopsies by General Pathologists: Diagnostic Discrepancy Rate Measured by Blinded Review
Bibliographic record
Abstract
CONTEXT: Slide review has been advocated as a means to reduce diagnostic error in surgical pathology and is considered an important component of a total quality assurance program. Blinded review is an unbiased method of error detection, and this approach may be used to determine the diagnostic discrepancy rates in surgical pathology. OBJECTIVE: To determine the diagnostic discrepancy rate for skin biopsies reported by general pathologists. DESIGN: Five hundred eighty-nine biopsies from 500 consecutive cases submitted by primary care physicians and reported by general pathologists were examined by rapid-screen, blinded review by 2 dermatopathologists, and the original diagnosis was compared with the review interpretation. RESULTS: Agreement was observed in 551 (93.5%) of 589 biopsies. Blinded review of these skin biopsies by experienced dermatopathologists had a sensitivity of 100% (all lesions originally reported were detected during review). False-negative errors were the most common discrepancy, but false positives, threshold discrepancies, and differences in type or grade were also observed. Only 1.4% of biopsies had discrepancies that were of potential clinical importance. CONCLUSIONS: Blinded review demonstrates that general pathologists reporting skin biopsies submitted by primary care physicians have a low diagnostic error rate. The method detects both false-negative and false-positive cases and identifies problematic areas that may be targeted in continuing education activities. Blinded review is a useful component of a dermatopathology quality improvement program.
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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 | 0.047 | 0.221 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".