Self-Monitoring of Blood Glucose (SMBG) in Insulin- and Non–Insulin-Using Adults with Diabetes: Consensus Recommendations for Improving SMBG Accuracy, Utilization, and Research
Bibliographic record
Abstract
Current clinical guidelines for diabetes care encourage self-monitoring of blood glucose (SMBG) to improve glycemic control. Specific protocols remain variable, however, particularly among non-insulin-using patients. This is due in part to efficacy studies that neglect to consider (1) the performance of monitoring equipment under real-world conditions, (2) whether or how patients have been taught to take action on test results, and (3) the physiological, behavioral, and social circumstances in which SMBG is carried out. As such, a multidisciplinary group of specialists, including several endocrinologists, a health psychologist, a diabetes nurse practitioner, and a patient advocate (the Panel), discuss within this review article how the potential of SMBG might be fully realized in today's healthcare environment. The resulting recommendations cover technological, clinical, behavioral, and research considerations with the aim of achieving short- and long-term benefits, ranging from fewer hypoglycemic episodes to lower complication-related costs. The panel also made suggestions for designing future studies that increase the ability to discern optimal models of SMBG utilization for individuals with diabetes who may, or may not, use insulin.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".