The Use of the International Academy of Cytology Yokohama System for Reporting Breast Fine-Needle Aspiration Biopsy
Bibliographic record
Abstract
OBJECTIVES: To perform the first meta-analysis regarding the pooled risk of malignancy (ROM) of each category of the Yokohama system for reporting breast fine-needle aspiration, as well as assess the latter's diagnostic accuracy using this new system. METHODS: Two databases were searched, followed by data extraction, study quality assessment, and statistical analysis. RESULTS: The "Insufficient," "Benign," "Atypical," "Suspicious," and "Malignant" Yokohama system categories were associated with a pooled ROM of 17% (95% CI, 10%-28%), 1% (95% CI, 1%-3%), 20% (95% CI, 17%-23%), 86% (95% CI, 79%-92%), and 100% (95% CI, 99%-100%), respectively. When both "Suspicious" and "Malignant" interpretations were regarded as cytologically positive, sensitivity (SN) was 91% (95% CI, 87.6%-93.5%) and false-positive rate (FPR) was 2.33% (95% CI, 1.30-4.14%). A summary receiver operating characteristic curve was constructed and the pooled area under the curve was 97.3%, while the pooled diagnostic odds ratio was 564 (95% CI, 264-1,206), indicating a high level of diagnostic accuracy. When only "Malignant" interpretations were regarded as cytologically positive, the pooled FPR was lower (0.75%; 95% CI, .39%-1.42%) but at the expense of SN (76.61%; 95% CI, 70.05%-82.10%). CONCLUSIONS: Despite Yokohama's system early success, more data would be needed to unravel the system's value in clinical practice.
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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 | 0.033 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".