Healthcare Professional Perceptions of Blood Glucose Meter Features That Support Achievement of Self-Management Goals Recommended by Clinical Practice Guidelines
Bibliographic record
Abstract
Background: Blood glucose meters remain an effective tool for blood glucose monitoring (BGM) but not all meters provide the same level of insight beyond the numerical glucose result. Objective: To investigate healthcare professional (HCP) perceptions of four meters and how these meters support the achievement of self-management goals recommended by diabetes clinical practice guidelines. Methods: Three hundred and fifty-three HCPs from five countries reviewed the features and benefits of four meters using interactive webpages and then responded to statements about the utility of each meter and ranked each meter in terms of clinical value. Results: Meter D ranked significantly higher in terms of clinical utility for all 13 guideline questions (70%-84%, P < .05) compared to other meters. Endocrinologists (69%-85%), primary care physicians (PCP; 63%-80%), and diabetes nurses (DN; 80%-89%) consistently ranked meter D highest for all guideline questions. DNs ranked selected questions significantly higher compared to PCPs (8 of 13) or endocrinologists (3 of 13; P < .05). Meter D achieved strong endorsement from HCPs in France and Germany, followed by the United States and Canada, with comparatively lower responses from Italian HCPs ( P < 0.05). With respect to self-management, 80% of HCPs selected meter D as their first choice for patients with type 1 diabetes to help patients improve diabetes management or understand their numbers to help them stay in range. Conclusions: HCPs had strong preference for a meter providing additional insights, messages, and guidance direct to the patient to support achievement of self-management goals recommended by diabetes clinical practice guidelines.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".