Self‐monitoring in Type 2 diabetes: a randomized trial of reimbursement policy
Bibliographic record
Abstract
AIM: Self-monitoring of blood glucose is often considered a cornerstone of self-care for patients with diabetes. We assessed whether provision of free testing strips would improve glycaemic control in non-insulin-treated Type 2 diabetic patients. METHODS: Adults with Type 2 diabetes, excluding those with private insurance or using insulin, were recruited through community pharmacies and randomized to receive free testing strips for 6 months or not; all patients received similar baseline education and a glucose meter. Primary outcome was change in HbA(1c) over 6 months. RESULTS: We randomized 262 patients (131 intervention and 131 control subjects). Mean age was 68.4 years (sd 10.9), 48% were male, mean duration of diabetes was 8.2 years (sd 7.2), 97% used oral glucose-lowering agents and mean baseline HbA(1c) was 7.4% (sd 1.2). After 6 months, we observed no difference in HbA(1c) between intervention and control patients, after adjusting for baseline HbA(1c)[adjusted difference 0.03, 95% confidence interval (CI) -0.16, 0.22; P = 0.78]. A per protocol analysis of study completers (152/262; 60%) yielded similar results. Intervention patients reported testing 0.64 days per week more often than control subjects (95% CI 0.18, 1.10; P = 0.007), although testing was not associated with better glycaemic control (Pearson r = -0.10, P = 0.12). CONCLUSIONS: Reducing financial barriers by providing free testing strips did not improve glycaemic control in patients with Type 2 diabetes not using insulin. Our results question the value of policies that reduce financial barriers to testing supplies in this population.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".