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Record W2965438961 · doi:10.1681/asn.2019020127

Referral for Kidney Transplantation in Canadian Provinces

2019· article· en· W2965438961 on OpenAlexafffundabout
Soojin Kim, John S. Gill, Greg Knoll, Patricia Campbell, Marcelo Cantarovich, Edward Cole, Bryce Kiberd

Bibliographic record

VenueJournal of the American Society of Nephrology · 2019
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsMcGill UniversityUniversity of AlbertaOttawa HospitalUniversity of OttawaCentre for Advancing Health OutcomesDalhousie UniversityUniversity Health NetworkUniversity of TorontoUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsTransplantationReferralKidney transplantationMedicineNephrologyFamily medicineIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Significance Statement In Canada, access to kidney transplantation requires referral to a transplant center, and selection of patients for transplant is in part a subjective process. The authors determined the incidence of transplant referral among incident patients with ESKD in Canada. Only 17% of incident patients with ESKD were referred within 12 months of starting dialysis, and transplant referral varied more than three-fold between provinces. Factors associated with a lower likelihood of referral included older age, female sex, and receiving dialysis >100 km from a transplant center, but not median household income or nonwhite race. The findings highlight the need to educate health care providers about the medical criteria for kidney transplantation and implement standards for referral, as well as the need for ongoing reporting of referral for transplantation in national registries. Background Patient referral to a transplant facility, a prerequisite for dialysis-treated patients to access kidney transplantation in Canada, is a subjective process that is not recorded in national dialysis or transplant registries. Patients who may benefit from transplant may not be referred. Methods In this observational study, we prospectively identified referrals for kidney transplant in adult patients between June 2010 and May 2013 in 12 transplant centers, and linked these data to information on incident dialysis patients in a national registry. Results Among 13,184 patients initiating chronic dialysis, the cumulative incidence of referral for transplant was 17.3%, 24.0%, and 26.8% at 1, 2, and 3 years after dialysis initiation, respectively; the rate of transplant referral was 15.8 per 100 patient-years (95% confidence interval, 15.1 to 16.4). Transplant referral varied more than three-fold between provinces, but it was not associated with the rate of deceased organ donation or median waiting time for transplant in individual provinces. In a multivariable model, factors associated with a lower likelihood of referral included older patient age, female sex, diabetes-related ESKD, higher comorbid disease burden, longer durations (>12.0 months) of predialysis care, and receiving dialysis at a location >100 km from a transplant center. Median household income and non-Caucasian race were not associated with a lower likelihood of referral. Conclusions Referral rates for transplantation varied widely between Canadian provinces but were not lower among patients of non-Caucasian race or with lower socioeconomic status. Standardization of transplantation referral practices and ongoing national reporting of referral may decrease disparities in patient access to kidney transplant.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.047
Threshold uncertainty score0.339

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.006
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.301
Teacher spread0.283 · 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

Citations41
Published2019
Admission routes3
Has abstractyes

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