Risk modification for diabetic patients. Are other risk factors treated as diligently as glycemia?
Bibliographic record
Abstract
BACKGROUND: The importance of glucose control is recognized both by patients with diabetes and their physicians. However, other preventative interventions, such as using medications to manage lipid and blood pressure levels, are underused for diabetic patients. OBJECTIVES: To determine whether patients with diligent glucose management are more likely to use medications that treat lipids and blood pressure. METHODS: Administrative data records were evaluated for all diabetic patients aged 65 or older residing in Ontario in 1999 without pre-existing coronary artery disease (n=161,553). Measures of diligent glucose management were insulin use and frequent capillary glucose testing ((3) 2 per day). Outcomes were prescription of a lipid-lowering drug or antihypertensive drug. Using multivariate modeling, odds ratios for each diligence measure were determined for each outcome, adjusting for age, sex, comorbidities, and other covariates. RESULTS: Patients using insulin did not have a clinically important difference in lipid-lowering drug use (adjusted odds ratio 0.9, 99% confidence interval 0.9 - 1.0, P=0.002) or antihypertensive drug use (adjusted odds ratio 1.1, 99% confidence interval 1.0 - 1.1, P<0.001) versus non-users. Adjusted odds ratios for frequent glucose testing were not significantly different from unity for either lipid-lowering or antihypertensive drug use. CONCLUSIONS: Patients who required and were capable of diligent glucose management, which is invasive, expensive and time-consuming, were no more likely to use medications to control lipids or blood pressure. Preventative care for patients with diabetes may be too focused on glycemic control, and may be neglecting the management of other cardiovascular risk factors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".