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Effect of a Novel Multicomponent Intervention to Improve Patient Access to Kidney Transplant and Living Kidney Donation

2023· article· en· W4388328596 on OpenAlexafffundabout
Amit Garg, Seychelle Yohanna, Kyla L. Naylor, Susan McKenzie, István Mucsi, Stephanie N. Dixon, Bin Luo, Jessica M. Sontrop, Mary Beaucage, Dmitri Belenko, Candice Coghlan, Rebecca Cooper, Lori Elliott, Leah Getchell, Esti Heale, Vincent Ki, Gihad Nesrallah, Rachel E. Patzer, Justin Presseau, Marian Reich, Darin Treleaven, Carol Wang, Amy D. Waterman, Jeffrey S. Zaltzman, Peter G. Blake

Bibliographic record

VenueJAMA Internal Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsSt. Michael's HospitalUniversity of OttawaTrillium Health CentreUniversity Health NetworkTranslational Research in OncologyWestern UniversityUniversity of TorontoHumber River Regional HospitalOntario Stroke NetworkMcMaster UniversityLondon Health Sciences CentreLawson Health Research Institute
FundersHealth CanadaAstellas PharmaKidney Foundation of CanadaSchulich School of Medicine and DentistryAcademic Medical Organization of Southwestern OntarioSchulich School of Medicine and Dentistry, Western UniversityLondon Health Sciences CentreCanadian Institutes of Health ResearchLawson Health Research Institute
KeywordsMedicineDialysisRandomized controlled trialIntervention (counseling)Kidney diseaseRandomizationKidney transplantationKidney transplantTransplantationDonationQuality of life (healthcare)Intensive care medicinePhysical therapyInternal medicineNursing

Abstract

fetched live from OpenAlex

Importance: 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. Objectives: To evaluate the effect of a multicomponent intervention designed to target several barriers that prevent eligible patients from completing key steps toward receiving a kidney transplant. Design, Setting, and Participants: This pragmatic, 2-arm, parallel-group, open-label, registry-based, superiority, cluster randomized clinical trial included all 26 CKD programs in Ontario, Canada, from November 1, 2017, to December 31, 2021. These programs provide care for patients with advanced CKD (patients approaching the need for dialysis or receiving maintenance dialysis). Interventions: Using stratified, covariate-constrained randomization, allocation of the CKD programs at a 1:1 ratio was used to compare the multicomponent intervention vs 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. Main Outcomes and Measures: The primary outcome was the rate of steps completed 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 included 20 375 potentially transplant-eligible patients with advanced CKD (intervention group [n = 9780 patients], usual-care group [n = 10 595 patients]). Despite evidence of intervention uptake, the step completion rate did not significantly differ between the intervention vs 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). Conclusions and Relevance: This novel multicomponent intervention did not significantly increase the rate of completed steps toward receiving a kidney transplant. Improving access to transplantation remains a global priority that requires substantial effort. Trial Registration: ClinicalTrials.gov Identifier: NCT03329521.

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)0.006
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 score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.020
GPT teacher head0.336
Teacher spread0.316 · 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

Citations27
Published2023
Admission routes3
Has abstractyes

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