Practice Variability and Unplanned Extubation Rates across Pediatric Intensive Care Units
Bibliographic record
Abstract
Abstract The purpose of this study was to describe the care of intubated patients in pediatric critical care. Acknowledging there are several perceived factors that contribute to unplanned extubations, a secondary objective was to describe how practice variation may relate to observed differences in unplanned extubation rates. A survey about practices related to the care of intubated patients was distributed to all pediatric intensive care units (PICUs) participating in the Virtual Pediatric Systems (VPS, LLC). Unplanned extubation rates for 2019 to 2020 were obtained from VPS. Univariate and bivariate analyses were performed to describe the responses, with unplanned extubation rates calculated as means. The text responses about perceived causes of unplanned extubation in participants' sites were explored using thematic content analysis. A total of 44 PICUs were included in this study (response rate 37.0%). The mean unplanned extubation rate for the sample was 0.41 (95% confidence interval: 0.31–0.50) per 100 intubation days. Variability was found across several aspects that impact care, including staffing, the frequency of procedures (e.g., chest radiography), and treatment-related goals (e.g., sedation and mobilization). The perceived causes of unplanned extubations in the sample included patient-, staff-, and equipment-related factors. We found practice variability in pediatric critical care units related to the care of intubated patients, which may contribute to the frequency of adverse events. As evidence emerges and professional associations and organizations recommend the best practices, knowledge translation will be required for the implementation and deimplementation of practices to improve the quality of care in PICUs.
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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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".