Blood glucose test strips: options to reduce usage
Bibliographic record
Abstract
BACKGROUND: Recent evidence suggests that, despite widespread use, self-monitoring of blood glucose levels has little clinical benefit in many patients with diabetes. The impact of more focused public-payer policies for the use of blood glucose test strips may be substantial. METHODS: We conducted a cross-sectional analysis of annual prescription claims for test strips between 1997 and 2008 for patients in Ontario aged 65 and older with diabetes. Patients were stratified into 1 of 4 hierarchical groups according to the most intensive glucose-lowering treatment received during each calendar year. Test strip use was calculated annually for each group over the study period, and the effects of 5 hypothetical policy scenarios of more selective test strip use were assessed. RESULTS: Test strip use increased by almost 250% from 1997 to 2008, with 52.6% (n = 263,513) of included patients receiving a prescription during 2008. Almost half of these patients were at low risk for drug-induced hypoglycemia. In 2008, over 117 million test strips were dispensed in Ontario; however, more focused policy scenarios could have reduced this number by between 9.5 million and 74.5 million test strips. INTERPRETATION: Many people who self-monitor their blood glucose are at relatively low risk for drug-induced hypoglycemia. The economic benefits associated with more selective testing could be redirected to more effective interventions for patients with diabetes.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".