Inflammatory myopathy with abundant macrophage [IMAM]: Systemic analysis and pathological approach to distinguish it from dermatomyositis
Bibliographic record
Abstract
Background: Inflammatory myopathy with abundant macrophage [IMAM] is marked by macrophage infiltration and muscle fibers damage, resembling dermatomyositis [DM] but with unique pathology. Its mechanism remains unclear. Our study focused on exploring the clinicopathological characteristics, underlying pathogenic mechanisms, and the challenges in diagnosing and managing IMAM. Methods: A systematic analysis of medical literature databases (Pub-med, Cochrane, Scopus, Google Scholar) was performed using the term “IMAM,” excluding studies on other inflammatory myopathies [IMs]. Selected studies were independently assessed with the Newcastle-Ottawa Scale, and quantitative data underwent inter-statistical analysis, descriptive and odds ratio, to identify relevant findings. Results: Eight studies, including 49 IMAM cases from 2003 to 2024, were analyzed. Five were case reports, and three were cross-sectional studies. IMAM showed no age or sex predilection. Common symptoms included proximal muscle weakness, pain, and fatigue, with atypical DM-like skin features in 65% of cases. Other association included hemophagocytosis, cutaneous panniculitis, and interstitial lung infiltration. Histologically, all cases showed myonecrosis infiltrated with CD68+ macrophages. Scattered CD3+ and CD4+ T-cells expressing IL-10 with no or rare CD8+ T-cells were identified. MAC deposition was limited to necrotic fibers, and perifascicular atrophy was absent in all cases. Anti-PL-7 and anti-U1 RNP antibodies were detected in 4% of cases. Elevated TNFα and IFN-γ levels, with low STAT1 and STAT6, were observed. Genetic analysis revealed MEFV polymorphisms in 7 cases and a TNFRSF1A mutation [C43R] in single case. Treatment involved steroids, with or without immunotherapy or chemotherapy, leading to remission and recovery in 43.7% of cases. Conclusion: IMAM is a distinct type of IMs that requires muscle biopsy for diagnosis as myositis antibody and cytokine tests are usually insensitive.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".