Intestinal Transplant Registry Report: Global Activity and Trends
Bibliographic record
Abstract
The Registry has gathered information on intestine transplantation (IT) since 1985. During this time, individual centers have reported progress but small case volumes potentially limit the generalizability of this information. The present study was undertaken to examine recent global IT activity. Activity was assessed with descriptive statistics, Kaplan-Meier survival curves and a multiple variable analysis. Eighty-two programs reported 2887 transplants in 2699 patients. Regional practices and outcomes are now similar worldwide. Current actuarial patient survival rates are 76%, 56% and 43% at 1, 5 and 10 years, respectively. Rates of graft loss beyond 1 year have not improved. Grafts that included a colon segment had better function. Waiting at home for IT, the use of induction immune-suppression therapy, inclusion of a liver component and maintenance therapy with rapamycin were associated with better graft survival. Outcomes of IT have modestly improved over the past decade. Case volumes have recently declined. Identifying the root reasons for late graft loss is difficult due to the low case volumes at most centers. The high participation rate in the Registry provides unique opportunities to study these issues.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| 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) | 0.002 | 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".