Kidney Transplant Fast Track and Likelihood of Waitlisting and Transplant
Bibliographic record
Abstract
Importance: Kidney transplant (KT) is the optimal treatment for end-stage kidney disease (ESKD). The evaluation process for KT is lengthy, time-consuming, and burdensome, and racial and ethnic disparities persist. Objective: To investigate the potential association of the Kidney Transplant Fast Track (KTFT) evaluation approach with the likelihood of waitlisting, KT, and associated disparities compared with standard care. Design, Setting, and Participants: This nonrandomized clinical trial was a prospective comparative cohort trial with a historical control (HC) comparison and equal follow-up duration at a single urban transplant center. Study duration was 2015 to 2018 for KTFT, with follow-up through 2022, and 2010 to 2014 for HC, with follow-up through 2018. Adult, English-speaking patients with ESKD, no history of KT, and a scheduled KT evaluation appointment were included. Among 1472 eligible patients for the KTFT group, 1288 consented and completed the baseline interview and 170 were excluded for not attending an evaluation appointment; among 1337 patients eligible for the HC group, 1152 consented and completed the baseline interview and none were excluded. Data were analyzed from August 2023 through December 2024. Exposure: Streamlined, patient-centered, coordinated-care KT evaluation process. Main Outcomes and Measures: Time to waitlisting for KT and receipt of KT. Results: The study included 1118 participants receiving KTFT (416 female [37.2%]; mean [SD] age, 57.2 [13.2] years; 245 non-Hispanic Black [21.9%], 790 non-Hispanic White [70.7%], and 83 other race or ethnicity [7.4%]) and 1152 participants in the HC group (447 female [38.8%]; mean [SD] age, 55.5 [13.2] years; 267 non-Hispanic Black [23.2%], 789 non-Hispanic White [68.5%], and 96 other race or ethnicity [8.3%]). After adjusting for demographic and clinical factors, the KTFT compared with the HC group had a higher likelihood of being placed on the active waitlist for KT (subdistribution hazard ratio [SHR], 1.40; 95% CI, 1.24-1.59). Among individuals who were waitlisted, patients in the KTFT vs HC group had a higher likelihood of receiving a KT (SHR, 1.21; 95% CI, 1.04-1.41). Black patients (SHR, 1.54; 95% CI, 1.11-2.14) and White patients (SHR, 1.38; 95% CI, 1.16-1.65) receiving KTFT were more likely to be waitlisted for KT than those in the HC group, but no such difference was found for patients with other race or ethnicity. Among Black patients, those with KTFT were more likely than those in the HC group to undergo KT (SHR, 1.52; 95% CI, 1.06-2.16), but no significant differences were found for White patients or those with other race or ethnicity. Conclusions and Relevance: This study found that KTFT was associated with a higher likelihood of waitlisting and KT than standard care. Findings suggest that KTFT may be associated with reduced disparities in KT by race and ethnicity. Trial Registration: ClinicalTrials.gov Identifier: NCT02342119.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".