Interobserver agreement of a probabilistic approach to reporting breast fine‐needle aspirations on ThinPrep®
Bibliographic record
Abstract
We have previously demonstrated the accuracy and reproducibility of a probabilistic/categorical approach for reporting breast fine-needle aspiration (FNA). However, the interobserver agreement in the application of this approach has not been assessed. Twenty breast FNA cases (each on one ThinPrep slide) were pulled from the cytology files of Beth Israel Deaconess Medical Center. The cases included benign epithelial proliferative lesions (6), DCIS (4), and infiltrating carcinoma (10), as shown by subsequent histology. Six pathologists with 14-25 yr of experience in interpreting breast FNA and 0-8 yr of experience with ThinPrep preparations rendered diagnoses according to the probabilistic approach. The kappa statistic for the unremarkable/proliferative, atypical, suspicious, and positive categories were 0.64, 0.08, 0.43, and 0.75, respectively (P < 0.001 for all except for the atypical category [P = 0.09]). Spearman's rho correlating the individual pathologist's diagnosis and the histologic diagnosis ranged from 0.51 (P = 0.02) to 0.78 (P < 0.0001). This was not correlated with the pathologists' years of experience interpreting breast FNA (P = 1.0) or with their years using ThinPrep preparations for breast FNA (P = 0.96). In conclusion, the interobserver agreement was excellent for the positive category in the probabilistic approach, poor for the atypical category, and fair to good for the other categories. The specific level of experience interpreting breast FNA or using ThinPrep among experienced pathologists did not seem to influence their accuracy in reporting the cases in our study.
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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 | 0.051 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".