Renal Transplantation in HIV-positive and HIV-negative People With Advanced Stages of Kidney Disease: Equity in Transplantation
Bibliographic record
Abstract
Abstract Background People with HIV are at a greater risk of end-stage kidney disease than the general population. Considering the risk of death after end-stage kidney disease, access to renal transplantation in people with HIV is critically important. Methods We included all adult patients on chronic dialysis in Ontario, Canada, between 1 April 2007 and 31 December 2020. We determined the probability of kidney transplantation with competing risk of death over time since the initiation of dialysis by calculating the adjusted subdistribution hazard ratios (sdHR; 95% confidence interval [CI]). We also compared long-term renal allograft and posttransplant mortality outcomes between HIV-negative and HIV-positive persons. Results Of 40 686 people (median age, 68 years; interquartile range, 57–77; 38.4% women), 173 were HIV-positive and 40 513 were HIV-negative. The incidence of kidney transplantation in HIV-negative and HIV-positive patients was 40.5 (95% CI, 39.4-41.6)/1000 person-years and 35.0 (95% CI, 22.8-53.7)/1000 person-years, respectively (P = .51). Considering the competing risk of death, HIV-positive people had a significantly lower chance of receiving kidney transplants than HIV-negative people (sdHR, 0.46 [95% CI, .30–.70]). The long-term allograft failure risk was not significantly different between HIV-negative and HIV-positive people, considering the competing risk of posttransplant death (sdHR, 1.71 [95% CI, .46-6.35]). Conclusions Although the incidence and crude probability of kidney transplantation were similar among HIV-negative and HIV-positive persons in this cohort, those with HIV had a significantly lower likelihood of kidney transplantation than those without HIV. Having HIV was not significantly associated with a poor long-term allograft outcome compared with patients without HIV.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".