Association of Primary Care Continuity with Home Dialysis, Transplantation, and Utilization of Medical Services for Patients Starting Hemodialysis
Bibliographic record
Abstract
Background: Primary care involvement may help patients starting dialysis with care coordination and support. It is unknown whether higher primary care physician (PCP) continuity associates with increased utilization of medical services or helps support patients towards home dialysis and kidney transplantation. Methods: Using administrative databases in Ontario, Canada, we conducted a population-based study of patients initiating maintenance hemodialysis between 2007 and 2017. We defined PCP continuity as a high usual provider of care index, >75% of PCP visits with the same PCP in the 2 years before dialysis (an established measure of PCP continuity). We used propensity scores to match patients with high and low continuity so that indicators of baseline health were similar. The primary outcomes were time to home dialysis (peritoneal or hemodialysis) and kidney transplantation, adjusted for the competing risk of death. Secondary outcomes included specialist visits, cancer screening, influenza vaccination, and measures of diabetes care. Results: We identified 9530 matched pairs. High PCP continuity was not associated with increased home dialysis transfer (14.0 events per 100 person-years versus 14.0 events per 100 person-years; hazard ratio 1.00; 95% CI 0.97-1.04) or kidney transplantation (4.3 events per 100 person-years versus 4.5 events per 100 person-years; hazard ratio 0.97; 95% CI 0.90-1.04). There were no differences in utilization of medical services, with the exception of high PCP continuity associated with greater colon cancer screening (hazard ratio 1.07, 95% CI 1.01-1.14), influenza vaccination (hazard ratio 1.33, 95% CI 1.27-1.39), and comprehensive diabetes care (hazard ratio 1.23, 95% CI 1.14-1.33). Conclusion: High PCP continuity during the transition to dialysis was not associated with increased utilization of home dialysis or transplantation and had only small effects on preventative services outside of influenza vaccination and comprehensive diabetes care. Given the competing health and time demands of patients on maintenance hemodialysis, additional work is needed to clarify how primary care may benefit this patient population. Funding: Government Support – Non-U.S.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".