Risk factors of pulmonary arterial hypertension in patients with systemic lupus erythematosus: A meta-analysis
Bibliographic record
Abstract
Objective To determine the risk factors of pulmonary arterial hypertension (PAH) related to systemic lupus erythematosus (SLE) through systematic reviews and meta-analyses. Methods We undertook electronic search strategies using Medline via PubMed, Embase, Web of Science, and Cochrane Library up to April 11, 2023. Study selection and data extraction were performed by 2 authors independently. We made risk of bias judgments based on the Newcastle-Ottawa Scale (NOS). Pooled risk ratios (RRs) and 95% confidence intervals (CIs) were calculated to estimate the overall effect sizes of potential risk factors for PAH in SLE patients. Univariate and multivariate meta-regression models were used to assess the independent effects of each risk factor on PAH. Sensitivity analyses were also conducted to explore potential sources of heterogeneity. Results A total of 19 articles were included in this meta-analysis, and the results showed that gender (female) [RR = 1.04, 95% CI (1.02, 1.06), p = .0001], interstitial lung disease [RR = 4.36, 95% CI (2.42, 7.85), p = .0001], alopecia [RR = 1.39, 95% CI (1.06, 1.83), p = .017], Raynaud’s phenomenon [RR = 1.83, 95% CI (1.41, 2.37), p = .0001], systemic hypertension [RR = 1.30, 95% CI (1.07, 1.58), p = .007], serositis [RR = 2.29, 95% CI (1.89, 2.77), p = .0001], pericardial effusion [RR = 3.33, 95% CI (2.20, 5.05), p = .0001], anti-RNP [RR = 1.86, 95% CI (1.19, 2.91), p = .006], anti-SSA [RR = 1.28, 95% CI (1.01, 1.62), p = .041], anti-SSB [RR = 1.38, 95% CI (1.19, 1.60), p = .0001], anti-U1RNP [RR = 1.58, 95% CI (1.07, 2.34), p = .023], thrombocytopenia [RR = 1.38, 95% CI (1.14, 1.68), p = .001], and current smokers [RR = 2.20, 95% CI (1.19, 4.06), p = .012] were all risk factors for PAH related to SLE. Conclusion PAH is a serious complication of SLE. Since prognosis of SLE patients after the occurrence of PAH is poor, routine examination should be conducted for SLE patients with PAH risk factors.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.068 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".