Chronic disease management: does the disease affect likelihood of care planning?
Bibliographic record
Abstract
Objective. To compare the demographic, socioeconomic, and medical characteristics of patients who had a General Practitioner Management Plan (GPMP) with those for patients without GPMP. Methods. Cohort study of patients with chronic diseases during the time period 1 July 2006 to 30 June 2008 using the Australian Department of Veterans’ Affairs (DVA) claims database. Results. Of the 88 128 veterans with chronic diseases included in the study, 23 015 (26%) veterans had a GPMP and 11 089 (13%) had a Team Care Arrangement (TCA). Those with a GPMP had a higher number of comorbidities (P < 0.001), and a higher use of services such as health assessment and medicine review (P < 0.001) than did those without GPMP. Diabetes was associated with a significantly increased use of GPMP compared with all other chronic diseases except heart failure. Conclusions. GPMPs are used in a minority of patients with chronic diseases. Use is highest in people with diabetes. What is known about the topic? Despite the fact that the Chronic Disease Management (CDM) program is appreciated by patients and allied health professionals, limited research has assessed how it is used in practice. What does this paper add? In the Veteran population, use of a General Practitioner Management Plan (GPMP) was associated with a higher number of comorbidities and of prior hospitalisations. Across chronic diseases use of GPMPs was low but was higher in people with diabetes. What are the implications for practitioners? Further research into the effect of CDM program on improvement of health outcomes is required.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".