Solid Breast Masses Diagnosed as Fibroadenoma at Fine-Needle Aspiration Biopsy: Acceptable Rates of Growth at Long-term Follow-up
Bibliographic record
Abstract
PURPOSE: To determine what growth rate is acceptable before recommending histologic diagnosis of solid breast lesions diagnosed as fibroadenoma at fine-needle aspiration biopsy (FNAB). MATERIALS AND METHODS: For 1,070 consecutive patients with breast lesions diagnosed as fibroadenoma at FNAB, three measurements of each mass were performed at the initial visit when FNAB was performed and at each follow-up ultrasonographic examination. Changes in volumes were calculated. At one or more visits, 194 masses showed an increase in volume. Nonfibroadenomas were excluded, and the data were used for comparison. Percentiles (90th and 95th) for percentage change in volume per month were used to determine acceptable changes in dimensions (specifically, greatest anteroposterior, parallel-to-skin, and perpendicular-to-skin dimensions). RESULTS: There were 567 interval measurements of 179 masses in 173 patients younger than 50 years and 50 measurements of 15 masses in 14 patients 50 years or older at the time of FNAB. The 95th percentile for percentage change in volume per month was approximately 16% for patients younger than 50 years; the 90th percentile was approximately 13% for patients 50 years or older. The 95th percentile mean change in dimension in a 6-month interval for those younger than 50 years was 20%; the 90th percentile change for those 50 years or older was also 20%. All excised masses with slower growth proved benign at histologic examination. CONCLUSION: Solid breast masses diagnosed as fibroadenomas at FNAB may be safely followed up if volume growth rate is less than 16% per month in those younger than 50 years and less than 13% per month in those 50 years or older. Acceptable mean change in dimension for a 6-month interval is 20% for all ages.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".