The Natural History of LDL Control in Type 2 Diabetes
Bibliographic record
Abstract
Despite randomized trials repeatedly showing the benefits of lowering LDL cholesterol with hydroxymethylglutaryl (HMG)-CoA reductase inhibitors (statins) (1–3), these medications are suboptimally used in type 2 diabetes (4–10). Although this care gap in type 2 diabetes has been frequently described in cross-sectional studies (6–11), it may be as informative to understand LDL control over time. In particular, there is growing recognition of care gaps in diabetes when comparing urban and academic settings with rural settings (11,12). Therefore, we examined changes over an 18-month period for adherence to guideline-recommended LDL cholesterol targets in a rural cohort with type 2 diabetes and determined the rates and correlates of 1 ) losing control of LDL cholesterol in those who were initially at target and 2 ) achieving control of LDL cholesterol in those who were not initially at target. The Diabetes Outreach Van Enhancement (DOVE) study was a controlled trial of a multifaceted intervention directed at health care providers to improve the quality of care for rural patients with type 2 diabetes in northern Alberta, Canada. The intervention consisted of an educational outreach (“academic detailing”) service, whereby specialist physicians promoted aggressive cardiovascular risk reduction for diabetes to primary care physicians. The study rationale, design, and outcomes have been previously published (12–16). All subjects provided written consent, and the study was approved by the University of Alberta. All patients had universal health care coverage and fee-for-service primary care physicians, with the nearest specialists being ∼6 …
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".