Controlled trial of an intervention to improve cholesterol management in diabetes patients in remote Aboriginal communities
Bibliographic record
Abstract
OBJECTIVES: Aboriginal communities have a high prevalence of diabetes and heart disease, and limited resources to address them. The objective of this study was to test the effectiveness of prioritizing care with audit and feedback on cholesterol management of diabetic patients. STUDY DESIGN: A controlled before-after intervention trial was conducted among health care providers in Oji-Cree reserves in Sioux Lookout Zone, Ontario. Two communities were randomized to receive an interactive educational workshop and chart audit with feedback on cholesterol management; 2 control communities received usual care. METHODS: The primary outcome measure used was the proportion of patients on statins, and the secondary outcome measure was the proportion of patients with LDL>2.5 mmol/L or TC/HDL>4.0 on statins. Outcomes were assessed by chart review at baseline and 10 months post-intervention. RESULTS: Patients in the 2 intervention communities (n=170) and the 2 controls (n=170) were comparable at baseline. The intervention did not increase the proportion of diabetic patients on statins overall or in the subset of patients with elevated cholesterol. The proportion of patients with elevated cholesterol on statins went from 46% to 53% (p=0.48) in the intervention group and from 47% to 50% (p=0.25) in the control group. CONCLUSIONS: Audit and feedback listing patients requiring treatment did not increase statin prescription rates in diabetic patients in remote Aboriginal settings. This may be due to elevated baseline rates, the low intensity of feedback and the constraints of the practice environment, such as low staffing and high staff turnover.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".