A Validation Study of an Expanded Description of Glandular (Acinar)/Tubule Formation for the Use of Nottingham Grading System for Invasive Breast Carcinomas Demonstrates Improvement in Concordance for Breast Pathologists and Trainees in Pathology
Bibliographic record
Abstract
Context.—: The Nottingham grading system, developed by Elston and Ellis, is the recommended method for grading invasive breast carcinoma. A previous study demonstrated the mean concordance for 35 breast pathologists in classifying 58 images as glandular (acinar)/tubule formation (G/TF) based on the World Health Organization definition was only 64%. Objective.—: To determine if an expanded description of G/TF according to the original definition and current use of the Nottingham grading system would improve recognition of G/TF among breast pathologists and pathologists in training. Design.—: Fifty-eight images with one structure circled were classified as G/TF or non-G/TF by Dr Ian Ellis. Images were sent as a PowerPoint (Microsoft) file to the breast pathologists who participated in the original study and to 21 trainees. Participants were asked to classify the structures based on the expanded description and were also provided with the 58 images from the first study with annotation. Results.—: Among the participating 28 of the original 35 breast pathologists, the mean concordance increased from 64% (range, 40%-97%) to 94% (range, 86%-100%). Trainees had a mean concordance of 90% (range, 52%-100%). Conclusions.—: The expanded description assisted in the recognition of G/TF for both breast pathologists and trainees. The most important impact on grading will likely be for carcinomas with complex cribriform patterns or micropapillary patterns with "inverted tubules." Participants endorsed that the expanded description of G/TF and the annotated images would be helpful reference material for pathologists.
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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 | 0.041 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".