Idiopathic granulomatous mastitis: a mimicking disease in a pregnant woman: a case report
Bibliographic record
Abstract
BACKGROUND: Idiopathic granulomatous mastitis is a rare, benign, inflammatory chronic condition of unclear etiology.This case is reported because it illustrates how idiopathic granulomatous mastitis can mimic other diseases, making it difficult to associate the presenting symptoms and the correct diagnosis; This disease is a challenge for clinicians to diagnose, manage and avoid iatrogenic complications, and requires consultation with experts in several specialties. CASE PRESENTATION: The patient was 30 years old, South-American, eleven weeks pregnant, and with an apparent infectious mastitis. She presented with progressive worsening of her breast symptoms and multiple negative laboratory tests. She suffered different side effects from several prescribed treatments and endured a prolonged recovery. The article emphasizes the need for ruling out common pathologies to arrive at the correct diagnosis such as bacterial and fungal infections; granulomatous conditions like tuberculosis and sarcoidosis; and inflammatory breast carcinoma. It also describes frequently used pharmacological and supplementary forms of treatment for patients with this condition. CONCLUSION: Idiopathic granulomatous mastitis is a rare unusual condition of unknown etiology. Pathological confirmation is required for its diagnosis and optimal management is still unclear. The presentation and management of this case is intended to advance its awareness to physicians from different specialties.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".