Clinical Benefit of Self-Monitoring of Blood Glucose Is Uncertain for Non–Insulin-Treated Patients With Type 2 Diabetes
Bibliographic record
Abstract
Allemann S, Houriet C, Diem P, Stettler C: Self-monitoring of blood glucose in non-insulin treated patients with type 2 diabetes: a systematic review and meta-analysis. Curr Med Res Opin 25:2903–2913, 2009 Design. A comprehensive systematic review and meta-analysis. Objective. To assess the effect of self-monitoring of blood glucose (SMBG) on A1C in non–insulin-treated patients with type 2 diabetes. Subjects. The analysis included 3,270 non–insulin-treated patients with type 2 diabetes in Canada, the United States, Europe, and Asia. In the 15 studies included, mean age ranged from 50 to 67 years, with 38–74% female patients. Mean duration of diabetes ranged from 0 to 12.5 years. Mean BMI ranged from 27.1 to 34.2 kg/m2, and baseline A1C ranged from 6.7 to 11.9%. Methods. Studies included randomized, controlled trials of non–insulin-treated type 2 diabetic patients comparing treatment strategies including SMBG to less frequent or no SMBG. To be included, trials also had to report data on A1C. Two independent reviewers assessed study quality, with any discrepancies resolved by a third reviewer. The authors used heterogeneity statistics (I2) to determine the appropriate model for analysis, with high heterogeneity (I2 > 80%) indicating that no pooled analysis should be done. The primary endpoint was A1C, and secondary outcomes were fasting glucose and the occurrence of hypoglycemia. The primary analysis was comparing patients performing SMBG with a non-SMBG control group. Secondarily, the authors compared more frequent SMBG with less frequent SMBG. Univariate meta-regression was used to assess the influence of other factors on the main outcomes of interest. Variables examined in the meta-regression included self-management instruction, use of a treatment algorithm, industry sponsorship, country of study origin, baseline mean A1C, study duration, and key domains of internal validity (intention to treat, allocation concealment, blinding of outcome assessors). Results. Of the 15 …
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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 | 0.039 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".