Comparison of the Radiologic and Clinical Findings of Adolescents With Breast Complaints
Bibliographic record
Abstract
INTRODUCTION: The American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS) was developed to guide imaging-based surgical treatment in patients with breast cancer. Studies confirming the BI-RADS did not include adolescents. To evaluate the validity of this classification system in adolescents, we aim to investigate the relationship between the BI-RADS and pathology findings in adolescents. METHODS: The medical data of 67 female adolescent patients, aged 12 to 18, referred to our clinic for breast-related complaints between 2013 and 2016 were reviewed retrospectively for demographic data and the results of radiologic and surgical pathologies. RESULTS: The main underlying reasons for the visit at the clinic were fullness, tenderness, pain, and palpable masses. Of the 67 patients, 46 were enrolled in the study. After breast ultrasonography, patients whose complaints had diminished were subtracted from the follow-up. The mean age of the patients was 16 years (12 to 18 y). The mean mass diameter size was 3.69 cm (0.9 to 15 cm), and the mean clinical follow-up was 65.3 days (11 to 1095 d). All the patients who were surgically intervened had benign pathology. Of the 21 surgically intervened patients with BI-RADS levels of ≥3, most had fibroadenomas. CONCLUSIONS: The BI-RADS classification-based treatment algorithm may not be valid in adolescents. In the present study, all patients with lesions with BI-RADS ≥3 levels had revealed benign pathologies. The BI-RADS classification may show an increased risk. However, to determine the need for a biopsy in adolescents, there is a need for larger-scale pediatric and adolescent studies using the BI-RADS classification.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".