Diabetes Medication Use and Cancer Risk: Protocol for a Systematic Review
Bibliographic record
Abstract
Abstract Background: Cancer is a growing public health challenge. Innovative approaches to prevent the future burden of cancer are needed. Diabetes medications may help to decrease the risk of cancers, however, a better understanding of relationships by cancer site and diabetes medication class are essential to guide future clinical trials. However, there is not adequate knowledge synthesis on different types of diabetes medications and cancer types. We aim to provide an integrated view of diabetes medications’ role and site-specific cancers.Methods: This systematic review will include observational studies (cohort, nested case-control, case-cohort, and case-control) and randomized controlled trials in human adults in which the effect of diabetes medication use on breast, lung, colorectal, prostate, liver, and pancreatic cancers was evaluated. The former four are among the most common cancer types, while liver and pancreatic cancer are of interest due to the biological roles of the liver and pancreas in blood glucose regulation. MEDLINE, Embase, Web of Science Core Collection, and Cochrane CENTRAL will be searched using a comprehensive and sensitive search strategy. The reference list of identified studies and relevant systematic reviews will be manually screened. Two reviewers will independently screen studies, extract data, and assess quality. Random-effect models will be employed to obtain overall pooled estimates of associations and corresponding 95% confidence intervals (CIs). This systematic review and meta-analyses will be reported following the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Results will be reported as specified by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.Discussion: Findings of this review will help to clarify relationships between diabetes medication and cancer, which is critical for future efforts to improve cancer prevention. Further, challenges and limitations identified in this review will foster opportunities to refine design and analysis procedures in future studies. Systematic review registration: The systematic review protocol was registered on Open Science Framework (https://osf.io/frg5z) and on PROSPERO (registration number CRD42021239348).
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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 | 0.051 | 0.078 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.021 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.095 | 0.009 |
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".