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Record W2315224003 · doi:10.1097/tp.0000000000000455

The Canadian Kidney Paired Donation Program

2014· article· en· W2315224003 on OpenAlexafffundabout
Edward Cole, Peter Nickerson, Patricia Campbell, Kathy Yetzer, Nick Lahaie, Jeffery Zaltzman, John S. Gill

Bibliographic record

VenueTransplantation · 2014
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsSt. Paul's HospitalSt. Michael's HospitalCentre for Advancing Health OutcomesUniversity of British ColumbiaCanadian Blood ServicesUniversity of Alberta
FundersCanadian Blood Services
KeywordsKidney donationDonationMedicineKidneyKidney transplantationInternal medicinePolitical science

Abstract

fetched live from OpenAlex

In Brief Background Establishment of a national kidney paired donation (KPD) program represents a unique achievement in Canada’s provincially organized health care system. Methods Key factors enabling program implementation included consultation with international experts, formation of a unique organization with a mandate to facilitate interprovincial collaboration, and the volunteer efforts of members of the Canadian transplant community to overcome a variety of logistical barriers. Results As of December 2013, the program had facilitated 240 transplantations including 10% with Calculated panel reactive antibody (cPRA) ≥97%. Unique features of the Canadian KPD program include participation of n = 55 nondirected donors, performance of only donor specific antibody negative transplants, the requirement for donor travel, and nonuse of bridge donors. Conclusion The national KPD program has helped maintain the volume of living kidney donor transplants in Canada over the past 5 years and serves as a model of inter-provincial collaboration to improve the delivery of health care to Canadians. This report highlights the experiences and accomplishments of a newly instituted kidney paired donor exchange program in Canada. The unique features and variables of the Canadian program suggest alternative ways to construct paired exchanges.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.011
GPT teacher head0.257
Teacher spread0.246 · 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

Citations59
Published2014
Admission routes3
Has abstractyes

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