Surfactant protein A as an important biomarker for interstitial lung disease in the assessment of occurrence, progression, acute exacerbation, and mortality: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Interstitial lung disease (ILD) is a large group of heterogeneous pulmonary disorders with complex etiologies. Noninvasive biomarkers serve as important tools for diagnosis and predicting prognosis of ILD. There is no comprehensive evidence for the clinical value of serum surfactant protein A (SP-A) in ILD population. METHODS: A systematical search was performed in PubMed, Web of Science, Cochrane Library, Embase and Scopus for English literatures published before May 1, 2024. The Newcastle-Ottawa scale was use for quality assessment of literatures. The weighted mean difference of serum SP-A in ILD with different disease states was summarized and analyzed. Sensitivity analysis was proceeded by sequentially excluding 1 study at a time and merging the remaining studies. Publication bias was judged by funnel plots, Egger's test and trim-and-fill method. RESULTS: A total of 22 studies comprising 2573 ILD patients were included in this meta-analysis. The results revealed that serum SP-A levels were significantly higher in ILD group compared to control group (weighted mean difference [WMD] = 29.82 ng/mL, 95% confidence interval [CI]: 17.15-42.59), serum SP-A levels of ILD patients in progression group were statistically higher than stable group (WMD = 18.36 ng/mL, 95% CI: 6.13-30.59), acute exacerbation group were significantly higher than non-acute exacerbation group (WMD = 16.47 ng/mL, 95% CI: 6.68-26.26), death group were distinctly higher than survival group (WMD = 23.63 ng/mL, 95% CI: 18.73-28.53) respectively. CONCLUSION: Elevated serum SP-A emerges as a pivotal noninvasive biomarker for assessing disease states and prognosis in ILD patients.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.050 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".