Association between diabetes or antidiabetic therapy and lung cancer: A meta‐analysis
Bibliographic record
Abstract
AIMS/INTRODUCTION: Diabetes can increase the risk of cancers at several sites, but the association between diabetes and lung cancer remains unclear. We aimed to provide the quantitative estimates for the association between diabetes or antidiabetic treatment and lung cancer risk in the present meta-analysis. MATERIALS AND METHODS: Cohort studies were identified by searching the PubMed database (January 1960 through October 2012) and manually assessing the cited references in the retrieved articles. Study-specific relative risks (RRs) and 95% confidence intervals (CIs) were estimated using a random-effects model. Study quality was assessed using the Newcastle-Ottawa scale. RESULTS: A total of 19 cohort studies were included in the present meta-analysis. Of these, 14 studies focused on the association between diabetes and lung cancer incidence, and seven studies focused on the association between antidiabetic treatment and lung cancer incidence. Compared with non-diabetic individuals, diabetic patients do not have an increased risk of lung cancer (RR = 1.04, 95% CI 0.87-1.24). The association between diabetes and lung cancer remained not statistically significant in subgroup analysis stratified by study characteristics, study quality, diabetes ascertainment or important confounders. A null association between insulin or biguanides therapy and lung cancer risk was found. However, the diabetic patients receiving thiazolidinedione (TZD) treatment had a 20% reduced risk of lung cancer than those without TZD treatment. CONCLUSIONS: No association between diabetes and lung cancer risk was found. However, TZD treatment might reduce lung cancer risk in diabetic patients.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.056 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 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".