Recurrent Multiple Fibroadenomas: History of a Case Presented in MDT Meeting With Clinical Discussion and Decision Making
Bibliographic record
Abstract
Background: Fibroadenoma is a common benign breast disorder in young women which has a low risk of malignant transformation. Most fibroadenomas present as a single mass, but the presence of multiple fibroadenomas can be seen in 15–20% of patients, with average number of 3–4 masses in one breast. In different studies and reports, various treatment modalities-including observation and follow up, surgery, radiofrequency ablation, etc- have been proposed, though the best management for these patients are not determined yet. Case presentation: We present the case of 33-year-old female with history of multiple bilateral benign breast lesions with a presumptive diagnosis of fibroadenomas. She had three previous surgical excisions in the past 14 years. Her case was presented to a breast MDT meeting to obtain a recommendation on appropriate management. Question: The proposed a question in MDT concerned the best and most appropriate management plan for the patient; Does she require further surgical excisions? And if not, how should she be followed? Conclusion: After reviewing past medical history, physical examination, and all documents regarding the patient, MDT members recommended that the patient should be managed with close follow up with physical examination and ultrasound every 6 months. The necessity of further surgical intervention would be determined according to any new findings.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".