Role of Corticosteroids along with Other Therapies for Treatment of Idiopathic Granulomatous Mastitis: A Narrative Review
Bibliographic record
Abstract
Background: The use of oral corticosteroids to treat idiopathic granulomatous mastitis (IGM) has been a point of controversy for a long time. In addition, a wide diversity of combinations with other therapies have been reported so far. This study aims to review the usage of oral corticosteroids and their combinations in the published literature. Methods: PubMed and Scopus were searched using the key word “granulomatous mastitis.” Citations were filtered in two stages, considering the titles/abstracts and full texts. Papers reporting the treatment of IGM with corticosteroids with/without other treatments were included. Results: Fifty-eight citations were included in this study, 31 of which had at least a group of patients treated only with systemic corticosteroids. Combination therapy of systemic steroids with immunosuppressants, surgical interventions, and antibiotics were used in 6, 12, and 13 studies, respectively. Conclusion: Considering the inconsistency of studies in reporting the severity of the disease, administered treatments, outcome of treatment, side effects, and follow-up, our study failed to provide solid evidence for using corticosteroids as the first step in the management of idiopathic granulomatous mastitis. There is still a need for further studies emphasizing the homogenization of such reports. In this regard, preparing a questionnaire to help homogenize future reports on IGM is highly recommended.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".