Does a Patient-Managed Insulin Intensification Strategy With Insulin Glargine and Insulin Glulisine Provide Similar Glycemic Control as a Physician-Managed Strategy? Results of the START (Self-Titration With Apidra to Reach Target) Study
Bibliographic record
Abstract
OBJECTIVE Diabetes self-management is universally regarded as a foundation of diabetes care. We determined whether comparable glycemic control could be achieved by self-titration versus physician titration of a once-daily bolus insulin dose in patients with type 2 diabetes who are unable to achieve optimal glycemia control with a basal insulin. RESEARCH DESIGN AND METHODS Patients with type 2 diabetes, an HbA1c level >7% (53 mmol/mol), and either nocturnal hypoglycemia episodes or an insufficient basal insulin glargine level (with or without oral agents) to achieve a fasting plasma glucose level ≤6 mmol/L (108 mg/dL) were studied. Participants all had bolus insulin glulisine added at breakfast and were allocated to either algorithm-guided patient self-titration or physician titration. The primary outcome was an HbA1c level ≤7% (53 mmol/mol) without severe hypoglycemia. RESULTS After a mean (SD) follow-up of 159.4 days (36.2 days), 28.4% of participants in the self-titration arm vs. 21.2% in the physician titration arm achieved an HbA1c level of ≤7% (53 mmol/mol) without severe hypoglycemia (between-group absolute difference 7.2%; 95% CI -3.2 to 17.7). The lower end of this 95% confidence interval was within the predetermined noninferiority boundary of -5% (P noninferiority = 0.011). CONCLUSIONS In stable patients with type 2 diabetes who are receiving doses of basal insulin glargine who require bolus insulin, a simple bolus insulin patient-managed titration algorithm is as effective as a physician-managed algorithm.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".