Clinical and demographic characteristics of patients receiving different oral hypoglycemic agents
Bibliographic record
Abstract
OBJECTIVE: To characterize the demographic and clinical profiles of older persons with diabetes treated with a thiazolidinedione (TZD) in comparison to other groups of patients, including all patients with diabetes and those taking other oral hypoglycemic agents (OHAs). RESEARCH DESIGN AND METHODS: We studied Ontario residents aged 66 and older and alive on 31 March 2008. Five groups were created based on diabetes status and history of treatment with OHAs, including patients prescribed rosiglitazone, patients prescribed pioglitazone, and a sample of three other groups: patients prescribed any other OHA in the preceding year, all patients with diabetes, and elderly Ontarians regardless of diabetes status. We excluded patients receiving insulin or multiple TZDs from the three OHA groups. Study groups were compared based on demographic information, measures of comorbidity, history of cardiovascular diseases, and concomitant use of drugs for cardiovascular disease. RESULTS: People treated with pioglitazone (n = 16 206) were highly similar to those treated with rosiglitazone (n = 16 066). Individuals treated with either TZD tended to be younger, less likely to reside in a long-term care facility, and had similar cardiovascular profiles to samples of patients with diabetes (n = 50 000) and those taking other OHAs (n = 50 000). CONCLUSIONS: Older patients started on TZDs have cardiovascular risk profiles comparable to other individuals with diabetes, including those taking other OHAs, suggesting that observational studies of TZD safety are not likely confounded by selection bias.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".