The Advantage of Multiple Listing for Deceased Donor Kidney Transplantation Is Increasing Over Time.
Bibliographic record
Abstract
In the United States, patients may be wait-listed for deceased donor kidney transplantation in multiple transplant centers. Using data SRTR from 1995-2010 we determined factors associated with multiple wait-listing in a multivariate logistic regression model. The independent association of multiple wait-listing with deceased transplantation during different time periods (1995-9, 2000-4, 2005-10) was determined using Cox multivariate regression after adjustment for differences in patient age, sex, race, cause of ESRD, BMI, PRA, ABO blood group, education, employment status, health insurance provider, and OPO waiting time. Results: Among 310,349 actively wait-list candidates, 8.0% were multiply wait-listed. Factors associated with multiple wait listing included recipient age 18-39 years (compared to those >40 years); males; non-Black race; non-diabetic, ABO blood group O, B (compared to blood group A), PRA > 30%; working full time; higher education > high-school; non-private insurance; primary listing in an OPO with median waiting time > 2 years. The table show that likelihood of transplantation in multiply wait-listed candidates compared to candidates only listed at a single increased over time. This translated into clinically significant differences in access to transplantation among multiply listed patients. For example the proportion of ABO blood group B patients (39% versus 29%); PRA >30% (48% versus 34%); and, patients listed in OPOs with waiting time > 2 years (41% versus 22%) that were transplanted after five years of wait-listing was significantly higher in multiply listed patients (p<0.001 for all comparisons). Conclusions: Patients with biological barrier to transplantation (i.e. blood type B and high PRA) as well as patients with primary listing in centers with waiting times > 2 years have a higher odds of multiple waitlisting. The policy of multiple wait-listing is associated with an increased likelihood of transplantation and this advantage is increasing over time. Given the organ shortage, ensuring that all difficult to transplant patients have an equal opportunity to take advantage of this policy is essential.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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".