Effect of beta-blockers on survival of lung cancer patients: a systematic review and meta-analysis
Bibliographic record
Abstract
The recent interest in beta-blockers as possible agents for drug repurposing in oncology arises from many pre-clinical and epidemiologic studies suggesting a possible clinically relevant antitumour effect. In lung cancer, given the contradictory results obtained, it is crucial to further study its effects. A systematic review of the literature was planned to evaluate a possible beneficial effect of beta-blocker on overall survival in lung cancer patients. Medline and Embase databases were searched from inception until 1 May 2018 to identify published studies that assessed the effect beta-blocker use on overall survival in lung cancer patients. Risk of bias was evaluated by Newcastle-Ottawa scale. Hazard ratios and 95% confidence intervals for overall survival were estimated using a random-effects model. Of 920 studies, seven (all retrospective and observational, six cohort and one case-control), including 7448 patients, met the inclusion criteria. Beta-blocker users with lung cancer had no increased overall survival compared to non-users (hazard ratio = 1.00; 95% confidence interval = 0.91-1.10; I = 45%). Similarly, beta-blocker users with non-small cell lung cancer had no increased overall survival compared to beta-blocker non-users (hazard ratio = 0.96; 95% confidence interval = 0.80-1.17; I = 56%). Our findings do not suggest an overall survival advantage in patients with lung cancer using beta-blocker therapy when compared to non-users. Further prospective cohort studies, designed to overcome the intrinsic limitations of retrospective observational studies are warranted to definitively clarify any possible beneficial effect of beta-blockers on lung cancer overall survival.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.006 | 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 | 0.002 |
| 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".