Opportunities To Improve Diabetes Care in the Hemodialysis Unit: A Cohort Study in Ontario, Canada
Bibliographic record
Abstract
Key Points Little is known about diabetes care gaps and predictors in patients using in-center hemodialysis. In Ontario, almost half of patients with diabetes on hemodialysis have diabetes care gaps; most commonly, gaps in retinopathy screening. Significant predictors of care gaps include younger age, female sex, shorter duration of diabetes, dementia, and fewer physician visits. Background Patients with diabetes receiving chronic, in-center hemodialysis face healthcare challenges. We examined the prevalence of gaps in their diabetes care, explored regional differences, and determined predictors of care gaps. Methods We conducted a population-based, retrospective study between January 1, 2016 and January 1, 2018 in Ontario, Canada. We included adults with prevalent diabetes mellitus receiving in-center hemodialysis as of January 1, 2018 and examined the proportion with ( 1 ) insufficient or excessive glycemic monitoring, ( 2 ) suboptimal screening for diabetes-related complications (retinopathy and cardiovascular screening), ( 3 ) hospital encounters for hypo- or hyperglycemia, and ( 4 ) hospital encounters for hypertension in the 2 years prior (January 1, 2016 to January 1, 2018). We then identified patient, provider, and health-system factors associated with more than one care gap and used multivariable logistic regression to determine predictors. Further, we used geographic information systems to explore spatial variation in gaps. Results There were 4173 patients with diabetes receiving in-center hemodialysis; the mean age was 67 years, 39% were women, and the majority were of lower socioeconomic status. Approximately 42% of patients had more than one diabetes care gap, the most common being suboptimal retinopathy screening (53%). Significant predictors of more than one gap included younger age, female sex, shorter duration of diabetes, dementia, fewer specialist visits, and not seeing a physician for diabetes. There was evidence of spatial variation in care gaps across our region. Conclusions There are opportunities to improve diabetes care in patients receiving in-center hemodialysis, particularly screening for retinopathy. Focused efforts to bring diabetes support to high-risk individuals might improve their care and 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".