Care gaps in achieving cholesterol targets in people with diabetes: A population-based study in a universal health care setting
Bibliographic record
Abstract
AIMS: Statins are first line therapy in people with diabetes. Little is known about real-world statin intensity use and low-density lipoprotein cholesterol (LDL-C) levels achieved. We aimed to describe statin intensity used, achievement of LDL-C targets, and factors associated with achieving targets among adults with diabetes. METHODS: This population based (∼4.3 million), retrospective observational study, used clinical and administrative databases. Statin use by intensity, adherence, and achievement of LDL-C targets in adults with diabetes were described. Multiple logistic regression assessed the factors associated with achieving targets. RESULTS: Out of 331,312 individuals with diabetes, 88% had an index LDL-C test. At follow up, 31% overall did not achieve LDL-C targets and overall adherence was 66%. Failure to achieve targets was 49%, 30%, and 25% in low-, moderate-, and high-intensity statin groups, respectively. Those who were older, males, had a history of myocardial infarction, stroke, congestive heart failure, renal disease, better adherence, and higher intensity statin users were more likely to achieve targets. CONCLUSIONS: One-third of people on statins did not achieve targets. Strategies to fill the gap between ideal and current levels of LDL-C achieved are needed if the benefits of statins demonstrated in trials are to be translated into practice.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".