Effect of a general practitioner management plan on health outcomes and hospitalisations in older patients with diabetes
Bibliographic record
Abstract
BACKGROUND: Little is known about the impact of a general practitioner management plan (GPMP) on health outcomes of patients with diabetes. AIM: To examine the impact of a GPMP on the risk of hospitalisation for diabetes. METHODS: A retrospective study using administrative data from the Australian Government Department of Veterans' Affairs was conducted (1 July 2006 to 30 June 2014) of diabetes patients either exposed or unexposed to a GPMP. The primary end-point was the risk of first hospitalisation for a diabetes-related complication and was assessed using Cox proportional hazard regression models with death as a competing risk. Secondary end-points included rates of receiving guideline care for diabetes, with differences assessed using Poisson regression analyses. RESULTS: A total of 16 214 patients with diabetes were included; 8091 had a GPMP, and 8123 did not. After 1 year, 545 (6.7%) patients with a GPMP and 634 (7.8%) of patients without a GPMP were hospitalised for a diabetes complication. There was a 22% reduction in the risk of being hospitalised for a diabetes complication (adjusted hazard ratio (HR) 0.78, 95% confidence interval (CI) 0.69-0.87, P < 0.0001) for those who received a GPMP by comparison to those who did not. Increased rates of diabetes guideline care, HbA1c claims (adjusted HR 1.29, 95% CI 1.25-1.33) and microalbuminura claims (adjusted HR 1.65, 95% CI 1.58-1.72) were observed after a GPMP. CONCLUSION: Provision of a GPMP in older patients with diabetes resulted in improved health outcomes, delaying the risk of hospitalisation at 12 months for diabetes complications. GPMP should be included as part of routine primary care for older patients with diabetes.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".