Bilateral male idiopathic granulomatous mastitis of the breast: a case report
Bibliographic record
Abstract
Abstract: Idiopathic granulomatous mastitis (IGM) is a rare benign inflammatory disease of the breast of unclear etiology, which can mimic breast cancer or infection. It is usually seen in parous women and is very seldom seen in males, with a few reported cases in the literature. We report a rare case of a 46-year-old male presenting with bilateral breast lumps and left-sided peri-areolar discharge from sinus tracts, who had failed antibiotic treatment. Bilateral mammogram, ultrasound, and core needle biopsies of the breast masses were performed, which revealed pathological findings consistent with granulomatous mastitis. The case was reported as bilateral IGM after exclusion of all known secondary causes of the disease, specifically tuberculosis and corynebacteria infection, as well as autoimmune diseases. To the best of our knowledge, this is the first case presentation of bilateral IGM in a male. Our patient was treated pharmacologically, initially using corticosteroids followed by methotrexate, and demonstrated good response with decreased swelling bilaterally and resolution of left peri-areolar discharge. Therefore, similar to women, IGM can present with bilateral disease in males. It is important for clinicians to be aware of IGM and its presentation in males to be able to correlate typical clinical findings with imaging and biopsy in order to avoid extended antibiotic therapy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".