Household income and LDL-C goal attainment in patients with diabetes and dyslipidemia in a Canadian dataset
Bibliographic record
Abstract
PURPOSE: There is evidence of a social disparity pertaining to the epidemiology and burden of illness of diabetes. The purpose of this study was to assess the association between household income strata and therapeutic goal achievement rates for LDL-cholesterol (LDL-C) (< 2.5 mmol/L) in Canadian diabetic patients. METHODS: Data (household income, cardiovascular risk factors, drug profile, clinical and laboratory variables) were obtained from a previous cross-sectional study of diabetic patients who filled a prescription for a lipid-lowering drug in selected pharmacies across Canada. Telephone interviews were conducted. Physicians, identified by the participating patients, were requested to complete a short questionnaire for clinical data. Achievement of LDL-C goals according to the Canadian diabetes guidelines were assessed and incorporated into regression models corresponding to household income strata. RESULTS: Seven household income strata were defined in the cohort (from less than 20,000 CDN$, up to 70,000 CDN$ by increments of 10,000 CDN$). LDL-C goals were attained in 34% of patients in the total cohort. There were no significant differences amongst household income strata for LDL-C goal achievement (p = 0.80). There were no significant differences in patient characteristics (age, sex, BMI) and cardiovascular risks according to the household income strata in this cohort, except age more than 65 in the lower income strata. CONCLUSION: This study demonstrates that household income was not a factor to achieve therapeutic goals for LDL-C for patients with diabetes in this dataset, although goal attainment was less than ideal overall. Future studies should address limitations of this work including small sample size, recruitment bias and lack of data on third party insurance coverage.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".