P0554 / #1652: LIBERATION FROM PICU-ACQUIRED COMPLICATIONS: BI-CENTER IMPLEMENTATION STUDY
Bibliographic record
Abstract
Aims & Objectives: To determine the feasibility and acceptability of implementing an early rehabilitation bundle “PICULiber8” at 2 tertiary care PICUs, McMaster’s Children Hospital and London Health Sciences Centre and to determine if “PICULiber8” reduce Pediatric ICU aquired complications (PACs) and long-term patient outcomes and is it cost effective. Methods: We used Pronovost’s 4 E’s Framework to implement the “PICULiber8 Bundle”. It consists of evidence-based practices to reduce sedatives, prevent withdrawal, manage delirium, optimize sleep, implement early mobilization, and engage families in the process. Engagement consisted of a pre-implementation survey followed by focus group interviews. A Bundle Development and Bundle Implementation teams were developed. A Delphi process was used. Evaluation will consist of assessing the impact of the bundle on the process of care, family satisfaction, clinical and patient-centered outcomes, and the cost using mixed methods and run chart. Results: Engagement was conducted in 3-months. Main key knowledge gaps were: awareness of delirium, under appreciation of PACs, practice variations with respect to sedation and goals in intubated children. Engagement data demonstrated the need for goal directed, evidence based and user-friendly guidelines and more consistency in approach to sedation amongst attending staff. Evidence-based Bundles were developed over a 4-month and an educational roll out plan was developed over the subsequent 3-months. Sequential roll-out of the guidelines were implemented over 2-months. Conclusions: It is feasible to implement an early rehabilitation bundle over a 12-month period in 2 sites using a clear implementation framework. Ongoing evaluation will measure the uptake of the bundle and its impact on process of care and patient outcomes.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".