Comparative Effectiveness of Hypoglycemic Medications Among Veterans
Bibliographic record
Abstract
BACKGROUND: The efficacy of diabetic medications among patients with multiple comorbidities is not tested in randomized clinical studies. It is important to monitor the performance of these medications after marketing approvals. OBJECTIVE: To investigate the risk of all-cause mortality associated with prescription of hypoglycemic agents. METHODS: We retrospectively examined data from 17,773 type 2 diabetic patients seen from March 2, 1998, to December 13, 2010, in 3 Veterans Administration medical centers. Severity was measured using patients' inpatient and outpatient comorbidities during the last year of visits. Severity-adjusted logistic regression was used to measure the odds ratio for mortality within the study period. RESULTS: Patients' severity of illness correctly classified mortality for 89.8% of the patients (P less than 0.0001). Being younger, married, and white decreased severity adjusted risk of mortality. Exposure to the following medications increased severity adjusted risk of mortality: glyburide (odds ratio [OR] = 1.804, 95% CI from 1.518 to 2.145), glipizide (OR = 1.566, 95% CI from 1.333 to 1.839), rosiglitazone (OR = 1.805, 95% CI from 1.378 to 2.365), chlorpropamide (OR = 3.026, 95% CI from 1.096 to 8.351), insulin (OR = 2.382, 95% CI from 2.112 to 2.686). None of the other medications (metformin, acarbose, glimepiride, pioglitazone, repaglinide, troglitazone, or dipeptidyl peptidase-4) were associated with excess mortality beyond what could be expected from the patients' severity of illness or demographic characteristics. The reported excess mortality could not be explained away by use of other concurrent, nondiabetic classes of medications. CONCLUSION: Our findings suggest chlorpropamide, glipizide, glyburide, insulin, and rosiglitazone increased severity-adjusted mortality in veterans with type 2 diabetes. A decision aid that could optimize selection of hypoglycemic medications based on patients' comorbidities might increase patients' survival.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".