Metformin use and survival in people with ovarian cancer: A population-based cohort study from British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: There is an active debate regarding whether metformin use improves survival in people with ovarian cancer. We examined this issue using methods designed to avoid immortal time bias-as bias that occurs when participants in a study cannot experience the outcome for a certain portion of the study time. METHODS: We used time-dependent analyses to study the association between metformin use for all 4,951 patients diagnosed with ovarian cancer in 1997 through 2018 in the province of British Columbia, Canada. Cox proportional hazards models were run to estimate the association between metformin and survival in the full cohort of ovarian cancer patients and among a cohort restricted to patients with diabetes. RESULTS: Metformin use was associated with a 17 % better ovarian cancer survival in the full cohort (adjusted hazard ratio (aHR) = 0.83 (95 %CI 0.67, 1.02)), and a 16 % better ovarian cancer survival for serous cancers patient's cohort (aHR = 0.84 (95 %CI 0.66, 1.07)), although both were not significant. However, a statistically significant protective effect was observed when restricting to the diabetic cohort (aHR = 0.71 (95 %CI 0.54-0.91)), which was also seen among serous cancers (aHR = 0.73 (95 %CI 0.54-0.98)). CONCLUSION: Metformin use was associated with improved ovarian cancer survival. The lack of statistical significance in the full cohort may reflect that diabetes is associated with reduced cancer survival, and thus diabetes itself may offset the benefit of metformin when examining the full cohort. Future research should examine metformin use among non-diabetic ovarian cancer 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".