Effectiveness of a Daily Rounding Checklist on Processes of Care and Outcomes in Diverse Pediatric Intensive Care Units Across the World
Bibliographic record
Abstract
BACKGROUND: Implementation of checklists has been shown to be effective in improving patient safety. This study aims to evaluate the effectiveness of implementation of a checklist for daily care processes into clinical practice of pediatric intensive care units (PICUs) with limited resources. METHODS: Prospective before-after study in eight PICUs from China, Congo, Croatia, Fiji, and India after implementation of a daily checklist into the ICU rounds. RESULTS: Seven hundred and thirty-five patients from eight centers were enrolled between 2015 and 2017. Baseline stage had 292 patients and post-implementation 443. The ICU length of stay post-implementation decreased significantly [9.4 (4-15.5) vs. 7.3 (3.4-13.4) days, p = 0.01], with a nominal improvement in the hospital length of stay [15.4 (8.4-25) vs. 12.6 (7.5-24.4) days, p = 0.055]. The hospital mortality and ICU mortality between baseline group and post-implementation group did not show a significant difference, 14.4% vs. 11.3%; p = 0.22 for each. There was a variable impact of checklist implementation on adherence to various processes of care recommendations. A decreased exposure in days was noticed for; mechanical ventilation from 42.6% to 33.8%, p < 0.01; central line from 31.3% to 25.3%, p < 0.01; and urinary catheter from 30.6% to 24.4%, p < 0.01. Although there was an increased utilization of antimicrobials (89.9-93.2%, p < 0.01). CONCLUSIONS: Checklists for the treatment of acute illness and injury in the PICU setting marginally impacted the outcome and processes of care. The intervention led to increasing adherence with guidelines in multiple ICU processes and led to decreased length of stay.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".