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Record W4409598952 · doi:10.1016/j.xkme.2025.101015

Association of Primary Care Continuity With Home Dialysis, Transplantation, and Utilization of Medical Services for Patients Starting Hemodialysis

2025· article· en· W4409598952 on OpenAlexafffundabout
Cole Wyman, Maya Djerboua, Kristin K. Clemens, Ziv Harel, Manish M. Sood, Samuel A. Silver

Bibliographic record

VenueKidney Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsOttawa HospitalUniversity of OttawaUniversity of TorontoSt. Michael's HospitalWestern UniversityQueen's University
FundersCanadian Institutes of Health ResearchSociety for Anthropological SciencesQueen's UniversityOntario Ministry of Health and Long-Term CareCanadian Society of NephrologyKidney Foundation of CanadaInstitute for Clinical Evaluative Sciences
KeywordsHemodialysisDialysisMedicineTransplantationPrimary careIntensive care medicineAssociation (psychology)Continuity of careInternal medicineFamily medicineHealth carePsychologyPolitical science

Abstract

fetched live from OpenAlex

Rationale & Objective Primary care may help patients starting dialysis with emotional support and access to health care services. It is unknown whether consistently visiting the same primary care physician (PCP) can strengthen patient confidence to select home dialysis, help facilitate medical appointments for transplantation, or increase care access. Study Design A population-based retrospective cohort study. Setting & Participants Patients initiating maintenance hemodialysis from 2007 to 2017 in Ontario, Canada. Exposure High PCP continuity using the usual provider of care index (an established measure of PCP continuity), defined as>75% of PCP visits with the same PCP in the 2 years before dialysis initiation. Outcomes Primary outcomes were time to home dialysis (peritoneal or hemodialysis) and transplantation. Secondary outcomes included specialist visits, cancer screening, influenza vaccination, and measures of diabetes care. Analytical Approach Propensity scores to match patients with high and low PCP continuity. Results We identified 9,530 matched pairs. High PCP continuity was not associated with increased home dialysis (14.0 events per 100 person-years vs 14.0 events per 100 person-years; subdistribution hazard ratio 1.00; 95% CI, 0.97-1.04) or transplantation (4.3 events per 100 person-years vs 4.5 events per 100 person-years; subdistribution hazard ratio 0.97; 95% CI, 0.90-1.04). High PCP continuity was 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). Limitations Residual confounding is possible. Conclusions High PCP continuity before dialysis initiation was not associated with increased utilization of home dialysis or transplantation but was associated with greater colon cancer screening, influenza vaccination, and comprehensive diabetes care. Additional work is needed to clarify how primary care may best benefit this patient population. Plain Language Summary Patients transitioning to maintenance hemodialysis require comprehensive health care. We investigated how closer relationships with a primary care physician (PCP) may complement nephrologists in caring for this patient population. We used administrative databases in Ontario, Canada, to conduct a retrospective population-based study, assessing how continuity with one PCP influenced the uptake of home dialysis, kidney transplantation, and patient access to health care services. We found that high PCP continuity before dialysis initiation was not associated with increased transition to home dialysis or kidney transplantation but did lead to increases in colon cancer screening, influenza vaccination, and diabetes assessment. Further work is required to determine how PCPs can help nephrologists best serve the multifaceted needs of patients transitioning to maintenance hemodialysis.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.259
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes3
Has abstractyes

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