The prognostic value of miR-126 expression in non-small-cell lung cancer: a meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related death. Growing evidence from recent studies have shown indicated that microRNA-126 (miR-126) played an important role in the progression of NSCLC. However, the potential value of miR-126 expression in prognosis of NSCLC remains to be fully elucidated. Here, we carried out a meta-analysis to assess the potential prognostic value of miR-126 for NSCLC. METHODS: PubMed, Embase, the Cochrane library, Web of Science, CNKI and WanFang database, as well as the reference of included studies, were searched to recognize pertinent studies until April 30, 2017. New castle-Ottawa scale was used to evaluate the quality of studies. Pooled hazard ratio (HR) with 95% confidence interval (CI) for overall survival (OS) was extracted by using a fixed-effects or a random-effects model on the basis of heterogeneity. Publication bias was evaluated by using Begg's tests. RESULTS: We identified four eligible trials involving 666 non-small-cell lung cancer patients in this meta-analysis. The results indicated that a high level of miR-126 played a favorable role in the overall survival (HR 0.73, 95% CI 0.61-0.86, fixed-effects model). There was no bias existed in this study. CONCLUSIONS: Our study showed that high expression level of miR-126 was a promising positive factor for OS for non-small cell lung cancer patients, and miR-126 might be a potential target for non-small-cell lung cancer therapy in the future.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it