Intestine Transplantation in the United States, 1999-2008
Bibliographic record
Abstract
Improving short-term results with intestine transplantation have allowed more patients to benefit with nearly 700 patients alive in the United States with a functioning allograft at the end of 2007. This success has led to an increase in demand. Time to transplant and waiting list mortality have significantly improved over the decade, but mortality remains high, especially for infants and adults with concomitant liver failure. The approximately 200 intestines recovered annually from deceased donors represent less than 3% of donors who have at least one organ recovered. Consent practice varies widely by OPTN region. Opportunities for improving intestine recovery and utilization include improving consent rates and standardizing donor selection criteria. One-year patient and intestine graft survival is 89% and 79% for intestine-only recipients and 72% and 69% for liver-intestine recipients, respectively. By 10 years, patient and intestine survival falls to 46% and 29% for intestine-only recipients, and 42% and 39% for liver-intestine, respectively. Immunosuppression practice employs peri-operative antibody induction therapy in 60% of cases; acute rejection is reported in 30%-40% of recipients at one year. Data on long-term nutritional outcomes and morbidities are limited, while the cause and therapy for late graft loss from chronic rejection are areas of ongoing investigation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".