Outcomes of Patients with Intestinal Failure after the Development and Implementation of a Multidisciplinary Team
Bibliographic record
Abstract
Aim. A multidisciplinary team was created in our institution to manage patients with intestinal failure (INFANT: INtestinal Failure Advanced Nutrition Team). We aimed to evaluate the impact of the implementation of the team on the outcomes of this patient population. Methods. Retrospective chart review of patients with intestinal failure over a 6-year period was performed. Outcomes of patients followed up by INFANT (2010-2012) were compared to a historical cohort (2007-2009). Results. Twenty-eight patients with intestinal failure were followed up by INFANT while the historical cohort was formed by 27 patients. There was no difference between the groups regarding remaining length of small and large bowel, presence of ICV, or number of infants who reached full enteral feeds. Patients followed up by INFANT took longer to attain full enteral feeds and had longer duration of PN, probably reflecting more complex cases. Overall mortality (14.8%/7.1%) was lower than other centers, probably illustrating our population of "early" intestinal failure patients. Conclusions. Our data demonstrates that the creation and implementation of a multidisciplinary program in a tertiary center without an intestinal and liver transplant program can lead to improvement in many aspects of their care.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".