Evaluating a novel pediatric critical care outreach program’s impact on ICU transfers and outcomes
Bibliographic record
Abstract
Background We evaluated the impact of the Transition Assessment Post Transfer (TAPT) program, a pediatric critical care outreach team (PCCOT) follow-up initiative, on morbidity and mortality outcomes for children who received a pediatric intensive care unit (PICU) consult in the emergency department (ED) and were transferred to an inpatient ward. Objectives The primary objective of this research was to evaluate the impact of the TAPT program on PICU admission rates. Secondary objectives included a description of patient characteristics, PICU length of stay, days on invasive and non-invasive ventilation, and mortality. Methods A retrospective analysis compared pre-TAPT (2014–2017) and post-TAPT (2017–2020) periods. Patients included were those requiring PICU admission within 24 hours of hospital admission from the ED. A subgroup analysis examined patients with a prior PICU consult in the ED. The intervention involved routine PCCOT follow-up for children after PICU consultation or PCCOT activation in the ED, introduced in July 2017. Results An interrupted time-series analysis of 3 years before and after TAPT implementation included 316 children (35% female; mean age 4.8 years) requiring unplanned PICU admission within 24 hours, with 22 (7.0%) having a prior PICU consult in the ED. There were no significant differences in early unplanned PICU transfers, morbidity indicators, or mortality. Conclusions There were no significant differences in PICU transfer rates between the pre- and post-TAPT periods. Further research is needed to improve risk identification for early deterioration and assess the broader impact of TAPT.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".