Circulating biomarkers and detection of pulmonary diseases in patients with systemic lupus erythematosus: A systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
BACKGROUND: Pulmonary diseases (PD) in systemic lupus erythematosus (SLE) are common and cover several entities. Diagnosing PD in SLE is often challenging, why reliable biomarkers are warranted. Several studies have explored the relationship between circulating biomarkers (CB) and detection of PD in SLE, but with conflicting results. OBJECTIVE: To investigate evidence supporting associations between CB and PD in SLE through a systematic literature review of observational studies. METHOD: We searched MEDLINE and EMBASE for studies addressing potential associations between CB and PD in SLE. Afterwards forward- and backward citation search was performed. Internal validity and risk of bias were addressed with Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Outcome Reporting Bias in Trials (ORBIT). Association between CB and PD across studies were investigated through meta-analyses and individual studies were summarized in tables. RESULTS: We identified 13,504 references; of these, 24 studies were eligible, including 1883 patients and 43 different CB. In individual studies 21 different CB were significantly associated with PD. Meta-analyses resulted in 10 associations of potential clinical significance between PD or PD-related outcomes and five CB (anti-double stranded DNA antibodies, anti-Ribonucleoprotein antibodies, anti-Smith antibodies, CC motif Ligand 21, and Interferon Gamma Inducible Protein 10). CONCLUSION: Through meta-analyses we identified CB that were significantly associated with PD in SLE including anti-dsDNA. Furthermore, anti-dsDNA, anti-Sm, anti-RNP, and CCL21 were associated with reduced pulmonary function in SLE. The results were rated with very low certainty of evidence, why they are hypothesis generating. Further studies addressing associations are needed.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".