Delays in Living Donor Transplantation Are Increasing Over Time.
Bibliographic record
Abstract
Given the health and economic consequences of even a relatively short exposure to dialysis, avoiding delays in living donor transplantation (LDTX) is desirable. With the exception of preemptive transplantation, few studies have examined factors associated with delays in LDTX. Using data from the USRDS we determined the time to LDTX from the date of first dialysis treatment among n = 38,343 non-preemptive LDTX recipients and documented the proportion of preemptive transplants over time. Using a multivariate logistic regression model we then identified factors associated with delayed LDTX (defined by dialysis exposure >18 months). The proportion of preemptive LDTX performed annually remained relatively stable during study period (mean 17%). In contrast, the proportion of delayed LDTX increased from 19% in 1995 to 45 % in 2007. After adjustment for biological factors (i.e. ABO, PRA, age, cause of ESRD, BMI, comorbid conditions) the odds of delayed transplantation were 2.7 fold greater in 2005-7 than in 1995-9 (see Table). Further a number of socio-demographic factors were associated with higher odds of delayed transplantation including non-white race, lower median household income, lower education, and lack of private insurance. Conclusions: There was a 2.7 fold increase in the odds of delayed LDTX between 1995-2007. Even among patients who successfully obtained a LDTX, socio-demographic factors impact access to LDTX. Strategies to increase the efficiency of evaluating donors and recipients for LDTX may result in significant healthcare savings and improved post transplant outcomes.Table: No Caption available.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 0.001 |
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".