The role of microRNAs as diagnostic and prognostic biomarkers of asbestos-related lung cancer: a systematic review and meta-analysis.
Bibliographic record
Abstract
Background: The prognosis for asbestos-related lung cancer (LC) and malignant pleural mesothelioma (MPM) is poor especially at advanced stage. Early diagnostic biomarkers might improve patients’ survival. Aim: To evaluate the role of microRNAs (miRNAs) as diagnostic and prognostic biomarkers for asbestos-related LC and MPM via a systematic literature review and meta-analysis. Methods: Electronic databases and grey literature were searched up to April 2023 using our review protocol registered in PROSPERO. Study quality was assessed via the Newcastle-Ottawa scale. For the most promising miRNAs, diagnostic accuracy was pooled as Area Under the Curve (AUC) in a meta-analysis. Results: Among the 331 articles retrieved from the search, 28 studies were included in the review, and 7 in the meta-analysis. Most studies were hospital-based case-control studies conducted in Europe, on MPM in men. MiR-126, miR-132-3p, and miR-103a-3p were the most promising diagnostic biomarkers for MPM with pooled AUCs of 85%, 73%, and 50%, respectively (Figure 1). Conclusion: Based on our review, specific miRNAs are associated with asbestos-related LC and MPM diagnosis and prognosis. Further large longitudinal studies are required to validate these findings. erj;64/suppl_68/PA2215/F1 F1 F1
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".