A retrospective evaluation of prescribing practices related to intensity of glycemic control among older adults with type-2 diabetes across Canada
Bibliographic record
Abstract
Background: Diabetes is highly prevalent among the elderly population. Optimal glucose management in this cohort remains ill-defined with high-quality evidence lacking and hypoglycemia risk a significant concern. The extent of overtreatment in Canada is not clearly established. Methods: This retrospective observational cohort study was conducted using primary care data between 2010-2017 from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) to assess proportions of over-treatment among patients with type-2 diabetes across Canada (phase-I). A further detailed analysis was performed using administrative population-based data of the Manitoba Centre for Health Policy (MCHP) to assess over-treatment in Manitoba (phase-II). Number of overall medications and annual HbA1c testing frequency were also assessed as a measure of burden. SAS® statistical software was used for the analyses. Results: Using CPCSSN data, overall rates of over-treatment were 7.0% in 2012 and 6.9% in 2016, while rates were significantly higher (20.4% (2012), 21.5% (2016)) using MCHP data. A considerable proportion with poorly-controlled diabetes ((41.9% (2012), 35.8% (2016) in phase-I) and (19% in 2012 & 10.5% in 2016 in phase-II)) were prescribed no medications, indicating under-treatment. The mean number of overall medications prescribed per patient was 4.4 (SD ± 4.5) in 2012 and 5.1 (SD ± 4.9) in 2016, and 39% and 41% were prescribed 5 or more medications in 2012 and 2016, respectively. Approximately 19% of patients were potentially over-tested, while just over 2% were potentially under-tested. Rates of over-treatment and over-testing were higher in those with advanced age and those with dementia. Conclusions: Potential over-treatment rates in this Canadian primary care population appeared lower compared to US studies. However, rates were found to be significantly higher in Manitoba using dispensation data compared to provincial & national primary care prescription data, with no evidence of rates decreasing over time. In contrast, there was a considerable proportion of poorly controlled patients who were potentially undertreated. Patients with advanced age and dementia appear to be over-treated and tested. These findings indicate a need for individually tailored personalized diabetes management in Canada.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".