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Record W4283705411 · doi:10.32768/abc.202293si279-286

Role of Corticosteroids along with Other Therapies for Treatment of Idiopathic Granulomatous Mastitis: A Narrative Review

2022· review· en· W4283705411 on OpenAlexaff
Abbas Mirzapour, Aida Allahyari, Sepehr Metanat, Sanaz Zand, Mohammadreza Tabary, Mojgan Karbakhsh, Ahmad Kaviani

Bibliographic record

VenueArchives of Breast Cancer · 2022
Typereview
Languageen
FieldMedicine
TopicMetastasis and carcinoma case studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsGranulomatous mastitisMedicineDermatologyMastitisNarrative reviewPsychological interventionIntensive care medicinePathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.907
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.328
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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