Achievement of treatment targets among patients with type 2 diabetes in 2015 and 2020 in Canadian primary care
Bibliographic record
Abstract
Background: An update on the degree to which patients with type 2 diabetes in Canada achieve treatment targets is needed to document progress and identify subgroups that need attention. We sought to estimate the frequency with which patients managed in primary care met treatment targets (i.e., HbA1c ≤ 7.0%, blood pressure < 130/80 mm Hg and low-density lipoprotein cholesterol [LDL-C] < 2.00 mmol/L), guideline-based use of statins and of angiotensin-convertingenzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs), and the effects of patient age and sex. Methods: We conducted a cross-sectional study of 32 503 and 44 930 adults with diabetes in Canada on June 30, 2015, and 2020, respectively, using electronic medical record data from primary care practices across 5 provinces. We grouped achievement of diabetes targets by age and sex, and compared between groups using logistic regression with adjustment for cardiovascular comorbidities. Results: In 2020, target HbA1c levels were achieved for 63.8% of women and 58.9% of men. Blood pressure and LDL-C targets were achieved for 45.6% and 45.8% of women, and for 43.1% and 59.4% of men, respectively. All 3 treatment targets were achieved for 13.3% of women and 16.5% of men. Overall, 45.3% and 54.0% of women and men, respectively, used statins; 46.5% of women used ACE inhibitors or ARBs, compared with 51.9% of men. With the exception of blood pressure and HbA1c levels among women, target achievement was lower among younger patients. Achievement of the LDL-C target, statin use and ACE inhibitor or ARB use were lower among women at any age. From 2015 to 2020, target achievement increased for HbA1c, remained consistent for LDL-C and declined for blood pressure; use of statins and of ACE inhibitors or ARBs also declined. Interpretation: Target achievement for blood pressure and use of statins and of ACE inhibitors and ARBs declined between 2015 and 2020, and was suboptimal in all patient groups. Widespread quality improvement is needed to increase evidence-based therapy for people 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".