Enrolment in primary care networks: impact on outcomes and processes of care for patients with diabetes
Bibliographic record
Abstract
BACKGROUND: Primary care networks are a newer model of primary care that focuses on improved access to care and the use of multidisciplinary teams for patients with chronic disease. We sought to determine the association between enrolment in primary care networks and the care and outcomes of patients with diabetes. METHODS: We used administrative health care data to study the care and outcomes of patients with incident and prevalent diabetes separately. For patients with prevalent diabetes, we compared those whose care was managed by physicians who were or were not in a primary care network using propensity score matching. For patients with incident diabetes, we studied a cohort before and after primary care networks were established. Each cohort was further divided based on whether or not patients were cared for by physicians enrolled in a network. Our primary outcome was admissions to hospital or visits to emergency departments for ambulatory care sensitive conditions specific to diabetes. RESULTS: Compared with patients whose prevalent diabetes is managed outside of primary care networks, patients in primary care networks had a lower rate of diabetes-specific ambulatory care sensitive conditions (adjusted incidence rate ratio 0.81, 95% confidence interval [CI] 0.75 to 0.87), were more likely to see an ophthalmologist or optometrist (risk ratio 1.19, 95% CI 1.17 to 1.21) and had better glycemic control (adjusted mean difference -0.067, 95% CI -0.081 to -0.052). INTERPRETATION: Patients whose diabetes was managed in primary care networks received better care and had better clinical outcomes than patients whose condition was not managed in a network, although the differences were very small.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| 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".