Treatment Discontinuation and Clinical Events in Type 2 Diabetes Patients Treated with Dipeptidyl Peptidase-4 Inhibitors or NPH Insulin as Third-Line Therapy
Bibliographic record
Abstract
OBJECTIVE: To compare dipeptidyl peptidase-4 (DPP-4) inhibitors with neutral protamine Hagedorn (NPH) insulin, in terms of effectiveness and safety for the management of patients with type 2 diabetes mellitus (DM2) not controlled on metformin and sulfonylureas. METHODS: A retrospective cohort study of individuals with DM2 newly dispensed with either DPP-4 inhibitors or NPH as third-line therapy, after metformin and sulfonylurea. Treatment discontinuation, macrovascular outcomes, and hypoglycemia were compared using multivariable Cox regression models, adjusted for sex, age, year of cohort entry, place of residence, hypertension, past history of hypoglycemia, diabetic ketoacidosis, comorbidities, and number of visits to emergency departments, outpatient physician, and hospitalizations. RESULTS: Treatment discontinuation and hypoglycemia occurred more frequently with NPH than with DPP-4 inhibitor users. In the adjusted Cox model, the use of NPH compared to that of DPP-4 inhibitors was associated with a higher risk of discontinuation (HR: 1.33; 95% CI 1.27-1.40) and hypoglycemia (HR: 2.98; 95% CI 2.72-3.28). Risk of cardiovascular events was similar across groups. CONCLUSIONS: This real-world analysis suggests that DM2 patients initiating third-line therapy with NPH have poorer control of diabetes when compared to DPP-4 inhibitor initiators.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".