The Role of Anti-U1 RNP Antibody in Connective Tissue Disease-Associated Pulmonary Arterial Hypertension: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Connective tissue disease (CTD) patients may suffer from pulmonary arterial hypertension (PAH), a serious complication, and anti-U1 ribonucleoprotein (RNP) antibodies can be used as a potential indicator for the development and prognosis of CTD-associated PAH (CTD-PAH). However, there are still some controversies; thus, a systematic review and meta-analysis were performed. We searched PubMed, Embase, Cochrane Library, and Scopus for eligible studies and assessed their quality using Newcastle−Ottawa scales or Agency for Healthcare Research and Quality indicators according to the type of research. Odds ratio (OR) was adopted as a measure of effect in risk factor analysis, and hazard ratio (HR) was adopted for prognostic factor analysis. Publication bias was evaluated using the Egger’s test. Thirteen studies were finally included. Anti-U1 RNP antibody was proved as a risk factor for PAH among CTD patients (OR = 5.30, 95%CI 2.96−9.48, p < 0.05) and a protective factor against mortality among CTD-PAH patients (HR = 0.55, 95%CI 0.36−0.83, p < 0.05). CTD patients with positive anti-U1 RNP antibodies are at high risk for PAH, so routine screening examinations, including echocardiography, are recommended. Additionally, anti-U1 RNP positivity has been linked to decreased mortality in patients with CTD-PAH.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".