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Record W4362657952 · doi:10.1681/asn.0000000000000133

Association of Primary Versus Rotating Nephrologist Model of Care in Hemodialysis Programs with Patient Outcomes

2023· article· en· W4362657952 on OpenAlexafffundabout
Kevin Yau, Nivethika Jeyakumar, Yuguang Kang, Stephanie N. Dixon, Megan Freeman, Amit X. Garg, Ziv Harel, Manish M. Sood, Alison Thomas, Ron Wald, Samuel A. Silver

Bibliographic record

VenueJournal of the American Society of Nephrology · 2023
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsKingston Health Sciences CentreOttawa HospitalUniversity of OttawaInstitute for Clinical Evaluative SciencesQueen's UniversityLondon Health Sciences CentreLawson Health Research InstituteUniversity of TorontoWestern UniversitySt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsNephrologyMedicineInternal medicineHemodialysisDialysisStaffingIntensive care medicinePopulationTransplantationPrimary careEmergency medicineFamily medicineNursing

Abstract

fetched live from OpenAlex

SIGNIFICANCE STATEMENT: Nephrologist staffing models for patients receiving hemodialysis vary widely. Patients may be cared for continuously by a single primary nephrologist or by a group of nephrologists on a rotating basis. It remains unclear whether these differing care models influence clinical outcomes. In this population-based cohort study of more than 14,000 incident patients on maintenance hemodialysis from Ontario, Canada, we found no difference in mortality, kidney transplantation, home dialysis initiation, hospitalizations, or emergency department visits when care was provided by a single primary nephrologist or a rotating group of nephrologists. These results suggest that primary nephrologist models do not necessarily improve objective clinical outcomes, providing reassurance to patients, providers, and administrators that both models are acceptable options.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.019
GPT teacher head0.269
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueJournal of the American Society of Nephrology→Same topicDialysis and Renal Disease Management→French-language works237,207→