Co‐morbidity and the utilization of health care for Australian veterans with diabetes
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of co-morbidity on health service utilization by Australian veterans with diabetes. METHODS: A retrospective cohort study was undertaken including veterans aged >or= 65 years dispensed medicines for diabetes in 2006. Data were sourced from the Australian Department of Veterans' Affairs health claims database. Utilization of preventive health services for diabetes was assessed, including claims for glycated haemoglobin (HbA(1c)) test, microabuminuria, podiatry services, diabetes care plans, medication reviews, case conferences, general practitioner (GP) management plans and ophthalmology/optometry services. RESULTS: Among the 17,095 veterans dispensed medicines for diabetes, more than 80% had four or more co-morbid conditions. Those with a higher number of co-morbidities were more likely to have had claims for optometry/ophthalmology services and podiatry services, but not for other services. Veterans with at least one diabetes-related hospital admission had no more claims for diabetes health services than those who had no diabetics-related hospital admission, except for endocrinology services (relative risk = 1.26, 95% confidence intervals 1.15-1.37). Veterans with dementia were less likely to have had claims for diabetes health services while patients with renal failure were more likely to have had claims for the services. CONCLUSIONS: Low utilization of preventive diabetes care services is apparent in all co-morbidity groups. Patients with renal failure or dementia used more and less health services resources, respectively. Given the high mean age of this population, there may be valid reasons for the low use, such as competing health demands and patients' preferences.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".