Systemic corticosteroids in fibrotic lung disease: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: We aimed to assess the available evidence for corticosteroids in fibrotic interstitial lung disease (fILD) to inform the randomised embedded multifactorial adaptive platform ILD. DESIGN: Systematic review and meta-analysis. DATA SOURCES: We searched Embase, Medline, Cochrane CENTRAL and Web of Science databases from inception to April 17 2023. ELIGIBILITY CRITERIA: We included studies that compared corticosteroids with standard care, placebo or no treatment in adult patients with fILD. DATA EXTRACTION AND SYNTHESIS: We report on the change in forced vital capacity (FVC) and mortality. We used random-effects meta-analysis to estimate relative risk (RR) for dichotomous outcomes, and mean difference (MD) and standardised MDs for continuous outcomes, with 95% CIs. RESULTS: Of the 13 229 unique citations identified, we included 10 observational studies comprising 1639 patients. Corticosteroids had an uncertain effect on mortality compared with no treatment (RR 1.03 (95% CI 0.85 to 1.25); very low certainty evidence). The effect of corticosteroids on the rate of decline in FVC (% predicted) was uncertain when compared with no treatment (MD 4.29% (95% CI -8.26% to 16.83%); very low certainty evidence). However, corticosteroids might reduce the rate of decline in FVC in patients with non-idiopathic pulmonary fibrosis (IPF) fILD (MD 10.89% (95% CI 5.25% to 16.53%); low certainty evidence), while an uncertain effect was observed in patients with IPF (MD -3.80% (95% CI -8.94% to 1.34%); very low certainty evidence). CONCLUSIONS: The current evidence on the efficacy and safety of corticosteroids in fILD is limited and of low certainty. Randomised trials are needed to address this significant research gap.
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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.042 |
| Bibliometrics | 0.007 | 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) | 0.004 | 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".