The Survival Effect of Metformin on Non‐Small Cell Lung Cancer Treated with Chemotherapy: A Systematic Review
Bibliographic record
Abstract
Objective. Metformin is a common antidiabetic drug that has been reported to serve as an anticancer agent in combination with other therapies. But the effect of the addition of metformin on the survival of non‐small cell lung cancer (NSCLC) patients undergoing chemotherapy is still controversial. We conducted this systematic review to evaluate the survival effect of metformin added to chemotherapy in NSCLC patients. Methods. Electronic literature search was performed in the PubMed, Embase, and Web of Science databases from their inception up to April 2023. The study region, study design, histological subtype of the NSCLC, tumor stage, treatment strategy, sample size, follow‐up duration, diabetes status, and HR of OS or PFS of the included studies were extracted. The quality was assessed through Cochrane collaboration’s tool for RCT and the Newcastle–Ottawa scale (NOS) for observational studies, respectively. Results and conclusions. Eleven studies with a total of 4606 patients were finally included. Five RCTs showed a high risk of bias due to the open‐label nature while six retrospective studies were of high quality. Two studies of NSCLC patients with diabetes reported significant benefits in overall survival from metformin addition, while one study of patients without diabetes reported a negative effect on the survival of metformin addition. The survival impact of metformin added to chemotherapy on unresectable NSCLC patients remains inconclusive. The survival benefit might be more prominent in patients with diabetes, awaiting further evidence.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".