Application of the Probabilistic Approach to Reporting Breast Fine Needle Aspiration in Males
Bibliographic record
Abstract
OBJECTIVE: To apply the probabilistic approach to a series offine needle aspiration (FNA) samples of male breast lesions and determine the accuracy and reproducibility of this method of reporting in men. STUDY DESIGN: All male breast surgical specimens with a preoperative breast FNA at our institution from 1994 to 2005 were identified. The FNAs were blindly reviewed by 2 groups of observers and classified in 1 of 5 categories using published reporting guidelines: positive, suspicious, atypical, proliferative without atypia and unremarkable. The histologic and cytologic diagnoses were correlated. The interobserver variation was determined. RESULTS: A total of 138 FNAs were performed for 123 male patients. Histologic correlation was available for 23 satisfactory FNAs. A total of 11 of 11 carcinomas (100%) were classified as positive, suspicious or atypical. Of 12 benign masses, 11 (91.6%) were classified as proliferative without atypia or unremarkable. One case of gynecomastia was classified as atypical by 1 observer but deemed not atypical with consensus review. The kappa statistic for benign and atypical/suspicious/malignant categories was 0.90. CONCLUSION: Based on this series, the probabilistic approach can be applied to the reporting of FNAs of male breast lesions. Gynecomastia may result in an atypical cytologic diagnosis.
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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 | 0.149 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".