Frequency of cancer events with saxagliptin in the <scp>SAVOR‐TIMI</scp> 53 trial
Bibliographic record
Abstract
The Saxagliptin Assessment of Vascular Outcomes Recorded in Patients with Diabetes Mellitus (SAVOR)-Thrombolysis in Myocardial Infarction (TIMI) 53 trial randomized trial of 16,492 patients (placebo, n = 8212; saxagliptin, n = 8280) treated and followed for a median of 2.1 years afforded an opportunity to explore whether there was any association with cancer reported as a serious adverse event. At least one cancer event was reported by 688 patients (4.1%): 362 (4.3%) and 326 (3.8%) in the placebo and saxagliptin arms, respectively (p = 0.13). There were 59 (0.6%) deaths adjudicated as malignancy deaths with placebo and 53 (0.6%) with saxagliptin. Stratification by gender, age, race and ethnicity, diabetes duration, baseline glycated haemoglobin and pharmacotherapy did not show any clinically meaningful differences between the two study arms. The overall number of cancer events and malignancy-associated mortality rates were generally balanced between the placebo and saxagliptin groups, suggesting a null relationship with saxagliptin use over the median follow-up of 2.1 years. Multivariable modelling showed that male gender, dyslipidaemia and current smoking were independent predictors of cancer. These randomized data with adequate numbers of cancer cases are reassuring but limited, by the short follow-up in a trial not designed to test this hypothesis.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".