Kidney and Pancreas Transplantation in the United States, 1997–2006: The HRSA Breakthrough Collaboratives and the 58 DSA Challenge
Bibliographic record
Abstract
Growth in the number of active patients on the kidney transplant waiting list has slowed. Projections based on the most recent 5-year data suggest the total waiting list will grow at a rate of 4138 registrations per year, whereas the active waiting list will increase at less than one-sixth that rate, or 663 registrations per year. The last 5 years have seen a small trend toward improved unadjusted allograft survival for living and deceased donor kidneys. Since 2004 the overall number of pancreas transplants has declined. Among pancreas recipients, those with simultaneous kidney-pancreas transplants experienced the highest pancreas graft survival rates. In response to the ongoing shortage of deceased donor organs, the US Health Resources and Services Administration launched the Organ Donation Breakthrough Collaborative in September 2003 and the Organ Transplantation Breakthrough Collaborative (OTBC) in October 2005. The 58 DSA Challenge is prominent among the goals adopted by the OTBC. Its premise: were each of the 58 existing donation service areas to increase the number of kidney transplants performed within their boundaries by 10 per month, an additional 7000 transplants over current annual levels would result. Such an increase could potentially eliminate the national kidney transplantation waiting list by 2030.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".