Effect of Metformin vs Placebo on and Metabolic Factors in NCIC CTG MA.32
Bibliographic record
Abstract
BACKGROUND: Metformin may improve metabolic factors (insulin, glucose, leptin, highly sensitive C-reactive protein [hs-CRP]) associated with poor breast cancer outcomes. The NCIC Clinical Trials Group (NCIC CTG) MA.32 investigates effects of metformin vs placebo on invasive disease-free survival and other outcomes in early breast cancer. Maintaining blinding of investigators to outcomes, we conducted a planned, Data Safety Monitoring Committee-approved, analysis of the effect of metformin vs placebo on weight and metabolic factors at six months, including examination of interactions with baseline body mass index (BMI) and insulin, in the first 492 patients with paired blood samples. METHODS: Eligible nondiabetic subjects with T1-3, N0-3, M0 breast cancer who had completed surgery and (neo)adjuvant chemotherapy (if given) provided fasting plasma samples at random assignment and at six months. Glucose was measured locally; blood was aliquoted, frozen, and stored at -80°C. Paired plasma aliquots were analyzed for insulin, hs-CRP, and leptin. Spearman correlation coefficients were calculated and comparisons analyzed using Wilcoxon signed rank test. All statistical tests were two-sided. RESULTS: Mean age was 52.1±9.5 years in the metformin group and 52.6 ± 9.8 years in the placebo group. Arms were balanced for estrogen/progesterone receptor, BMI, prior (neo)adjuvant chemotherapy, and stage. At six months, decreases in weight and blood variables were statistically significantly greater in the metformin arm (vs placebo) in univariate analyses: weight -3.0%, glucose -3.8%, insulin -11.1%, homeostasis model assessment -17.1%, leptin -20.2%, hs-CRP -6.7%; all P values were less than or equal to .03. There was no statistically significant interaction of change in these variables with baseline BMI or insulin. CONCLUSIONS: Metformin statistically significantly improved weight, insulin, glucose, leptin, and CRP at six months. Effects did not vary by baseline BMI or fasting insulin.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".