NeoCHIRP: A model for intestinal rehabilitation in the neonatal intensive care unit
Bibliographic record
Abstract
Although most children with IF are identified in the neonatal intensive care unit (NICU), IR teams may not be involved at this stage. We describe our collaborative model, blending NICU and IR expertise to optimize care. Over 6 years, the NeoCHIRP (Neonatal Children's IR Program) team followed 164 babies for weekly visits (median, 8; range, 1-27). Bedside rounds included CHIRP team physician and surgeons, neonatologist champion, attending neonatologist and fellow, NICU dietitian, bedside nurse, and family. Medical and nutrition status, nutrition history, and laboratory data were discussed, and a nutrition plan to support IR, considering the child's other medical needs, was created to guide the next week's management. Typical issues addressed included parenteral nutrition (PN) composition, enteral nutrition plan, oral feeding, management of small-intestinal bacterial overgrowth and sodium status, and cholestasis. A total of 164 babies were followed by the NeoCHIRP team. Of 153 survivors, IF resolved by discharge in 89% (136 of 153). Seventeen of 153 babies (11%) went on to require home PN and were transferred from NICU directly to the CHIRP team. By discharge, 99% of babies were orally fed (69/136, 50% fully, 67/136, 49% partially), and cholestasis improved or resolved in 80/105 (76%). Eleven babies (7%) died; four deaths were unrelated to IF, but in seven babies, IF was at least a contributing factor. In this high-risk cohort, most babies achieved good outcomes, and those who required longer-term IR transitioned smoothly to the CHIRP team.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".