Understanding diagnostic variability in breast pathology: lessons learned from an expert consensus review panel
Bibliographic record
Abstract
AIMS: To gain a better understanding of the reasons for diagnostic variability, with the aim of reducing the phenomenon. METHODS AND RESULTS: In preparation for a study on the interpretation of breast specimens (B-PATH), a panel of three experienced breast pathologists reviewed 336 cases to develop consensus reference diagnoses. After independent assessment, cases coded as diagnostically discordant were discussed at consensus meetings. By the use of qualitative data analysis techniques, transcripts of 16 h of consensus meetings for a subset of 201 cases were analysed. Diagnostic variability could be attributed to three overall root causes: (i) pathologist-related; (ii) diagnostic coding/study methodology-related; and (iii) specimen-related. Most pathologist-related root causes were attributable to professional differences in pathologists' opinions about whether the diagnostic criteria for a specific diagnosis were met, most frequently in cases of atypia. Diagnostic coding/study methodology-related root causes were primarily miscategorizations of descriptive text diagnoses, which led to the development of a standardized electronic diagnostic form (BPATH-Dx). Specimen-related root causes included artefacts, limited diagnostic material, and poor slide quality. After re-review and discussion, a consensus diagnosis could be assigned in all cases. CONCLUSIONS: Diagnostic variability is related to multiple factors, but consensus conferences, standardized electronic reporting formats and comments on suboptimal specimen quality can be used to reduce diagnostic variability.
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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 | 0.551 | 0.582 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".