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Record W4407863276 · doi:10.1681/asn.20233411b2c

Effect of a Multi-Component Intervention to Improve Patient Access to Kidney Transplantation and Living Kidney Donation

2023· article· en· W4407863276 on OpenAlexaffabout
Amit Garg, Seychelle Yohanna, Kyla L. Naylor, Susan McKenzie, István Mucsi, Stephanie N. Dixon, Bin Luo, Jessica M. Sontrop, Peter G. Blake

Bibliographic record

VenueJournal of the American Society of Nephrology · 2023
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsPublic Health OntarioUniversity Health NetworkUniversity of TorontoLawson Health Research InstituteMcMaster UniversityWestern University
Fundersnot available
KeywordsKidney donationKidney transplantationMedicineComponent (thermodynamics)DonationIntervention (counseling)TransplantationNephrologyKidneyArtificial kidneyIntensive care medicineInternal medicineNursingPolitical science

Abstract

fetched live from OpenAlex

Background: Patients with advanced chronic kidney disease (CKD) have the best chance for a longer and healthier life if they receive a kidney transplant. However, many barriers prevent patients from receiving a transplant. Methods: We conducted a pragmatic, two-arm, parallel-group, cluster-randomized trial of a multi-component intervention designed to target several barriers which prevent kidney transplantation and living donation. The trial included all 26 CKD programs in Ontario, Canada, from Nov 2017 to Dec 2021. These programs care for patients with advanced CKD (patients approaching the need for dialysis or receiving maintenance dialysis). Using covariate-constrained randomization, we allocated the CKD programs (1:1) to provide the intervention or usual care for 4.2 years. The intervention had 4 main components: (1) administrative support to establish local quality improvement teams; (2) transplant educational resources; (3) an initiative for transplant recipients and living donors to share stories and experiences; and (4) program-level performance reports and oversight by administrative leaders. The primary outcome was a composite of all completed steps toward receiving a kidney transplant. Each patient could complete up to 4 steps: step 1, referred to a transplant center for evaluation; step 2, had a potential living donor contact a transplant center for evaluation; step 3, added to the deceased donor waitlist; and step 4, received a transplant from a living or deceased donor. Results: The 26 CKD programs (13 intervention, 13 usual care) during the trial period cared for 20 375 potentially transplant-eligible patients with advanced CKD (intervention [n=9780 patients], usual care [n=10 595 patients]). Despite evidence of intervention uptake, the step completion rate did not significantly differ between the intervention versus usual-care groups: 5334 vs. 5638 steps; 24.8 vs. 24.1 steps per 100 patient-years; adjusted hazard ratio 1.00 (95% CI, 0.87-1.15). Results were consistent in multiple analyses. Conclusions: This province-wide strategy did not increase the rate of completed steps toward receiving a kidney transplant. Improving access to transplantation remains a global priority. Future efforts can build on lessons learned. Protocol: PMID 33948191 Protocol, Process evaluation: PMID 35340770 Statistical analytic plan: PMID 36438439 ClinicalTrials.gov record: NCT03329521 Funding: Commercial Support - Partnership grant funding received from Astellas Canada, Government Support - Non-U.S.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.013
GPT teacher head0.311
Teacher spread0.298 · 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 designNon-randomized trial
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

Citations0
Published2023
Admission routes2
Has abstractyes

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