Risk factors associated with <i>Pneumocystis jirovecii</i> pneumonia in juvenile myositis in North America
Bibliographic record
Abstract
OBJECTIVES: Pneumocystis jirovecii pneumonia (PJP) is associated with significant morbidity and mortality in adult myositis patients; however, there are few studies examining PJP in juvenile myositis [juvenile idiopathic inflammatory myopathy (JIIM)]. The purpose of this study was to determine the risk factors and clinical phenotypes associated with PJP in JIIM. METHODS: An research electronic data capture (REDCap) questionnaire regarding myositis features, disease course, medications and PJP infection characteristics was completed by treating physicians for 13 JIIM patients who developed PJP (PJP+) from the USA and Canada. Myositis features and medications were compared with 147 JIIM patients without PJP (PJP-) from similar geographic regions who enrolled in National Institutes of Health natural history studies. RESULTS: PJP+ patients were more often of Asian ancestry than PJP- patients [odds ratio (OR) 8.7; 95% CI 1.3, 57.9]. Anti- melanoma differentiation associated protein 5 (MDA5) autoantibodies (OR 12.5; 95% CI 3.0, 52.4), digital infarcts (OR 43.8; 95% CI 4.2, 460.2), skin ulcerations (OR 12.0; 95% CI 3.5, 41.2) and interstitial lung disease (OR 10.6; 95% CI 2.1, 53.9) were more frequent in PJP+ patients. Before PJP diagnosis, patients more frequently received pulse steroids, rituximab and more immunosuppressive therapy compared with PJP- patients. Seven PJP+ patients were admitted to the intensive care unit and four patients died due to PJP or its complications. CONCLUSIONS: PJP is a severe infection in JIIM that can be associated with mortality. Having PJP was associated with more immunosuppressive therapy, anti-MDA5 autoantibodies, Asian race and certain clinical features, including digital infarcts, cutaneous ulcerations and interstitial lung disease. Prophylaxis for PJP should be considered in juvenile myositis patients with these features.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".