Are Guideline-concordant Processes of Care Consistent Across the Rural–Urban Continuum? A Retrospective Cohort Study of Adults Newly Treated for Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVES: Our aim in this study was to identify the association between place of residence (metropolitan, urban, rural) and guideline-concordant processes of care in the first year of type 2 diabetes management. METHODS: We conducted a retrospective cohort study of new metformin users between April 2015 and March 2020 in Alberta, Canada. Outcomes were identified as guideline-concordant processes of care through the review of clinical practice guidelines and published literature. Using multivariable logistic regression, the following outcomes were examined by place of residence: dispensation of a statin, angiotensin-converting enzyme inhibitor (ACEi) or angiotensin II receptor blocker (ARB), eye examination, glycated hemoglobin (A1C), cholesterol, and kidney function testing. RESULTS: Of 60,222 new metformin users, 67% resided in a metropolitan area, 10% in an urban area, and 23% in a rural area. After confounder adjustment, rural residents were less likely to have a statin dispensed (adjusted odds ratio [aOR] 0.83, 95% confidence interval [CI] 0.79 to 0.87) or undergo cholesterol testing (aOR 0.86, 95% CI 0.83 to 0.90) when compared with metropolitan residents. In contrast, rural residents were more likely to receive A1C and kidney function testing (aOR 1.14, 95% CI 1.08 to 1.21 and aOR 1.17, 95% CI 1.11 to 1.24, respectively). ACEi/ARB use and eye examinations were similar across place of residence. CONCLUSIONS: Processes of care varied by place of residence. Limited cholesterol management in rural areas is concerning because this may lead to increased cardiovascular outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".