Weaning from Ventilation: A Developing Role for Pediatric Intensive Care Unit (PICU) Nurses? Evidence from two Cochrane Reviews
Bibliographic record
Abstract
Background Mechanical ventilation (MV) carries potential risks to mortality and morbidity; therefore, weaning should not be delayed. To safely reduce ventilator support, practice has transitioned from individual preference to a structured approach with guidelines. Objectives To highlight international challenges in developing PICU nurses’ role in weaning children from MV by reviewing the prevalence of, and evidence for, weaning protocols, and the current state of nurses’ roles and responsibilities in ventilator weaning. Main body Protocolised weaning has shown some success in reducing MV duration in adults and children. Consequently protocols have gained popularity with surveys reporting their use in 56–69% of European adults ICUs and 18% of UK PICUs. Findings from two systematic reviews show support for weaning protocols in adults, but that cannot yet be said regarding children. There are only a small number of randomised trials of protocolised weaning in children; they used diverse protocols and reported discordant findings making it impossible to pool results. Internationally, there is insufficient information about PICU nurses’ role in weaning, but a recent UK survey reported that nurses rarely titrated ventilator settings. It is possible that reticence to actively engage in the weaning process is linked to associated risks with pediatric extubation, but does not explain why nurses cannot progress weaning to the point of extubation. Key challenges If paediatric nurses are to confidently engage in the process of weaning they require suitable training and support. Developing appropriate protocols may be an important vehicle for safely changing practice in this respect.
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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 | 0.015 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".