Implementation strategies used to reduce Unplanned Extubations (UPE) in the Neonatal Intensive Care Unit at The Hospital for Sick Children (SickKids)
Bibliographic record
Abstract
This study analyzed unplanned extubation (UPE) reduction strategies as well as patient characteristics and other risk factors for all UPEs that occurred between January 1, 2007 and December 31, 2019 in the neonatal intensive care unit (NICU) at the Hospital for Sick Children (SickKids) in Toronto, Canada. Six major implementation strategies decreased UPEs per 100 ventilator days from 2.38 to 0.45 between 2003 and 2019. The study sample included 302 UPEs (252 infants) with 12% infants with repeated UPEs. The study analyzed the association of NICU UPEs with previous UPE history, birth weight, gestational age, taping protocol, procedures prior to UPE, sedation concern, patient restraint, type of endotracheal tube, loose tape, length of mechanical ventilation and length of NICU stay. These findings would be helpful for other healthcare facilities and researchers to inform the development of UPE reduction frameworks, and to improve 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".