Effect of metformin versus placebo on weight and metabolic factors in initial patients enrolled onto NCIC CTG MA.32, a multicenter adjuvant randomized controlled trial in early-stage breast cancer (BC).
Bibliographic record
Abstract
1033 Background: MA.32 investigates effects of Metformin vs. Placebo, in addition to standard care, on invasive disease free survival and other outcomes. Metformin may improve obesity and metabolic factors [insulin, glucose, leptin, C-reactive protein (CRP)] that have been associated with poor BC outcomes. Maintaining blinding of investigators to outcomes, we conducted a planned, DSMB approved, analysis of the effect of Metformin vs. Placebo on weight and metabolic factors at 6 months, including examination of interactions with baseline body mass index (BMI), in the first 498 subjects with paired fasting plasma samples. Methods: 498 non-diabetic subjects with T1-3, N0-3, M0 BC meeting defined entry criteria who had completed surgery and adjuvant chemo (if given) provided fasting blood samples at randomization and 6 months (while on study drug). Glucose was measured locally; blood was aliquoted, frozen and stored at -80°C then shipped to NCIC CTG (Kingston, Canada). Paired plasma aliquots were shipped to Mount Sinai Hospital (Toronto, Canada) for analysis of Insulin (Dako), hsCRP (Roche Elecsys) and leptin (Luminex). Statistical analysis used the Wilcoxon signed rank test. Results: Mean age was 52.4 ±9.2 years. Arms were balanced for ER/PgR (63% pos), BMI, prior adjuvant chemo (89%), T and N status, grade, mastectomy/lumpectomy and radiation. Conclusions: Metformin significantly improved weight, insulin, glucose, leptin and CRP at 6 months. Effects did not vary by baseline BMI. Funded by: NIH, CCSRI, CBCF, BCRF, Apotex Canada (drug & placebo - in kind). Clinical trial information: NCT01101438. [Table: see text]
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".