The quality of diabetes care among cancer survivors: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: As cancer survivorship continues to improve, management of co-morbid diabetes has become an increasingly important determinant of health outcomes for people with cancer. This study aimed to compare indicators of diabetes quality of care between people with diabetes and without a history of cancer. METHODS: We used the Electronic Medical Record Administrative data Linked Database (EMRALD), a database of Ontario primary care EMR charts linked to administrative data, to identify people with diabetes and at least 1 year follow-up. Persons with a history of cancer were matched 1:2 on age, sex and diabetes duration to those without cancer. We compared recommended diabetes quality of care indicators between persons with and without cancer using a matched cohort analysis. RESULTS: Among 229,627 people with diabetes, we identified 2275 people with cancer and 4550 matched controls; 86.5% had diabetes diagnosed after cancer. Compared to controls, cancer people with diabetes were significantly less likely to receive ACE inhibitors or angiotensin receptor blockers (OR 0.75 [95% CI 0.64-0.89]), receive statin therapy if age 50-80 years (OR 0.79 [95% CI 0.68-0.92]) and achieve an LDL cholesterol level <2.0 mmol/L (OR 0.82 [95% CI 0.74-0.91]). There were no differences in recommended clinical testing or achieving A1C and blood pressure targets between groups. CONCLUSION: Cancer survivors with diabetes are less likely to receive recommended cardiovascular risk-reducing therapies compared to people with diabetes without cancer of similar age, sex and diabetes duration. Further studies are warranted to determine if these associations are linked to worse survival, cardiovascular outcomes and quality of life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".