Impact of Intestinal Rehabilitation Program and Its Innovative Therapies on the Outcome of Intestinal Transplant Candidates
Bibliographic record
Abstract
OBJECTIVES: The outcome of children with intestinal failure has improved during the past decade following the introduction of novel therapies by dedicated intestinal rehabilitation programs (IRP). The aim of the present study was to assess the impact of IRP on the outcome of intestinal transplant (IT) candidates and the transplant waiting list. METHODS: A retrospective cohort study of children assessed for IT (n = 84) during a 10-year period. Comparisons were made among the following 3 time periods: before the establishment of our center's IRP (1999-2002; n = 33), early IRP (2003-2005; n = 18), and late IRP (2006-2009; n = 33). The following endpoints were used: patient outcome following IT assessment (not listed, listed and removed from the list, received transplant, died while on the list), patient characteristics at IT assessment, and patient status at the end of the study. RESULTS: The late-IRP era was associated with an increase in patients who were not listed (42% vs 28% at other periods, P = NS) and patients who were removed from the IT waiting list because of clinical improvement (P < 0.0005), and a decrease in those who died before transplant (15% vs >60% at other periods, P < 0.0005). The cause of death shifted from traditional causes such as liver failure or sepsis to other comorbid conditions (P < 0.005). Improved liver function at listing was also observed during late IRP (P < 0.005). CONCLUSIONS: Treatment by IRP, coupled with recent advances in the medical management of intestinal failure, is associated with improved survival and outcome of patients waiting for IT, and may lead to overall reduction in the number of IT in the future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".