Expression of estrogen receptor beta and overall survival in non-small cell lung cancer patients
Bibliographic record
Abstract
BACKGROUND: Lung cancer is the leading cause of cancer-related deaths among males and the second leading cause among females worldwide. Numerous studies have linked estrogen status to lung cancer outcome. However, there are studies with conflicting results about the effect of ERβ on survival of lung cancer. The aim of this meta-analysis is to evaluate the prognostic impact of estrogen receptor beta expression on survival among NSCLC patients. METHODS: We will search 15 electronic databases, including PubMed, Web of Science, EMBASE, Cochrane Library, and CNKI from inception to June 1, 2019. We will include all cohort studies comparing overall survival of NSCLC patients with high or low estrogen receptor beta expression. The database searches will be supplemented by searching through citations and references. Two reviewers will independently screen search results to identify eligible articles, complete data collection, and conduct quality assessment. All disagreements will be resolved by an independent third reviewer. Methodological quality of the included studies will be assessed using the Newcastle- Ottawa scale. Discrepancies will be resolved by consensus or by consulting a third author. Meta-analyses will be performed, and findings will be reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) and the meta-analysis of observational studies in epidemiology (MOOSE) guidelines. RESULTS: The results will be submitted to a peer-reviewed journal for publication. CONCLUSION: This review will provide a comprehensive evaluation of the evidence on the prognostic impact of ERβ expression among NSCLC patients and will help clinicians find potential treatments based on estrogen signaling.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".