Music Use for Sedation in Critically ill Children (MUSiCC trial): study protocol for a pilot randomized controlled trial
Bibliographic record
Abstract
Abstract Background Stress induced by pain and anxiety is common in pediatric intensive care unit (PICU) patients. Sedation/analgesia in PICU is usually achieved through various analgesics and sedatives. Excessive use of these drugs can put patients at risk for hemodynamic/respiratory instability, prolonged ventilation, withdrawal, delirium, and critical illness polyneuromyopathy. The use of non-pharmacologic interventions has been recommended by sedation guidelines. However, non-pharmacological measures in PICU, including music and noise reduction, have been inadequately studied. Methods The Music Use for Sedation in Critically ill Children (MUSiCC trial) pilot study is an investigator-initiated, three-arm, randomized controlled trial (RCT) on the use of music for sedation in PICU. The main goal of the study is to demonstrate feasibility of a music trial in PICU and to obtain the necessary information to plan a larger trial. The study compares music versus noise cancelation versus control in sedated and mechanically ventilated children admitted to PICU. In the music group, children receive the music (modified classical music) three times a day for 30 min at a time. Music is delivered with noise cancelation headphones. The noise cancelation group receives the same intervention but with a no music (sham playlist). The control group receives usual care with no specific intervention. Children remain in the study until extubation or a maximum of 7 days. The primary outcomes of the study are feasibility and sedation/analgesia requirements. Secondary outcomes include change in vital signs before and during the intervention, ICU delirium, and adverse effects related to the intervention. The estimated sample size is 20 subjects per group for a total of 60 children. Discussion Despite being recommended by current guidelines, evidence to support the use of music in PICU is lacking. Music has the potential to reduce sedation requirements and their negative side effects. This pilot RCT will demonstrate feasibility and provide the necessary information to plan a larger trial focusing on the effectiveness of the intervention. Trial registration The study was registered at ClinicalTrials.gov (NCT03497559) on April 13, 2018.
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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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.055 | 0.008 |
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".