Patient clustering in primary care settings
Bibliographic record
Abstract
Objective To determine whether neighbours who share the same family physicians have better cardiovascular and health care outcomes. Design Retrospective cohort study using administrative health databases. Setting Ontario. Participants The study population included 2,690,482 adult patients cared for by 1710 family physicians. Interventions Adult residents of Ontario were linked to their family physicians and the geographic distance between patients in the same panel or list was calculated. Using distance between patients within a panel to stratify physicians into quintiles of panel proximity, physicians and patients from close-proximity practices were compared with those from more-distant-proximity practices. Age- and sex-standardized incidence rates and hazard ratios from cause-specific hazards regression models were determined. Main outcome measures The occurrence of a major cardiovascular event during a 5-year follow-up period (2008 to 2012). Results Patients of panels in the closest-proximity quintile lived an average of 3.9 km from the 10 closest patients in their panel compared with 12.4 km for the 10 closest patients of panels in the distant-proximity quintile. After adjusting for various patient and physician characteristics, patients in the most-distant-proximity practices had a 24% higher rate of cardiovascular events (adjusted hazard ratio=1.24 [95% CI 1.20 to 1.28], P<.001) than patients in the closest-proximity practices. Age- and sex-standardized all-cause mortality and total per patient health care costs were also lowest in the closest-proximity quintile. In sensitivity analyses restricted to large urban communities and to White long-term residents, results were similar. Conclusion The better cardiovascular outcomes observed in close-proximity panels may be related to a previously unrecognized mechanism of social connectedness that extends the effectiveness of primary care practitioners.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".