Initiation of insulin therapy in elderly patients taking oral antidiabetes drugs
Bibliographic record
Abstract
BACKGROUND: We sought to estimate the rate of initiation of insulin therapy among elderly patients using oral anti-diabetes drugs and to identify the factors associated with this initiation. METHODS: We conducted a population-based cohort study involving people aged 66 or more years who were newly dispensed an oral antidiabetes drug. Individuals who had received acarbose or a thiazolidinedione were excluded. The rate of insulin initiation was calculated by use of the Kaplan-Meier method. Factors associated with insulin initiation were identified by multivariable Cox regression analyses. RESULTS: In this cohort of 69,674 new users of oral antidiabetes drugs, insulin was initiated at rate of 9.7 cases per 1000 patient-years. Patients who had initially received an insulin secretagogue (rather than metformin), who were prescribed an oral antidiabetes drug by an endocrinologist or an internist, who received higher initial doses of an oral antidiabetes drug, who received oral corticosteroids, used glucometer strips, or were admitted to hospital in the year before initiation of oral antidiabetes therapy, or who received 16 or more medications were more likely than those without these characteristics to have insulin therapy initiated. In contrast, patients who received thiazides or who used up to 12 medications (v. none) were less likely to have insulin therapy initiated. INTERPRETATION: Several factors related to drugs and health services are associated with the initiation of insulin therapy in elderly patients receiving oral antidiabetes drugs. It is unclear whether these factors predict secondary failure of oral antidiabetes drugs or instead reflect better management of type 2 diabetes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".