Diagnostic accuracy of circulating miR-126 for malignant pleural mesothelioma: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Circulating microRNAs are novel diagnostic markers for various types of cancer. Several studies have investigated the diagnostic accuracy of circulating miR-126 for malignant pleural mesothelioma (MPM), but the results varied. Therefore, we performed a systematic review and meta-analysis to investigate the diagnostic value of circulating miR-126 for MPM. METHODS: The PubMed database was searched to identify potentially eligible studies published before October 2020. The studies investigating the diagnostic value of circulating miR-126 for MPM were included in a systematic review and meta-analysis. A bivariate model was used to pool eligible studies' sensitivity and specificity. The revised tool for the quality assessment of diagnostic accuracy studies (QUADAS-2) was used to assess eligible studies' quality. RESULTS: Four studies with 156 MPM patients and 459 controls were included in this systematic review and meta-analysis. The pooled diagnostic sensitivity and specificity of circulating miR-126 for MPM were 0.71 and 0.69, respectively. A high risk of bias was observed in the domains of patient selection, index test, and flow and timing. CONCLUSIONS: Circulating miR-126 has limited value for diagnosing MPM. Considering that the available studies have a high risk of bias, further rigorous studies are needed to assess the diagnostic value of circulating miR-126 for MPM.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".