Physical Restraints in Critically Ill Children: A Multicenter Longitudinal Point Prevalence Study*
Bibliographic record
Abstract
OBJECTIVES: We elucidate to investigate the prevalence of and factors associated with the use of physical restraints among critically ill or injured children in PICUs. DESIGN: This was a multicenter, longitudinal point prevalence study. SETTING: We included 26 PICUs in Japan. PATIENTS: Included children were 1 month to 10 years old. We screened all admitted patients in the PICUs on three study dates (in March, June, and September 2019). INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: We collected prevalence and demographic characteristics of critically ill or injured children with physical restraints, as well as details of physical restraints, including indications and treatments provided. A total of 398 children were screened in the participating PICUs on the three data collection dates. The prevalence of children with physical restraints was 53% (211/398). Wrist restraint bands were the most frequently used means (55%, 117/211) for potential contingent events. The adjusted odds of using physical restraint in patients 1-2 years old was 2.3 (95% CI, 1.3-4.0) compared with children less than 1 year old. When looking at the individual hospital effect, units without a prespecified practice policy for physical restraints management or those with more than 10 beds were more likely to use physical restraints. CONCLUSIONS: The prevalence of physical restraints in critically ill or injured children was high, and significant variation was observed among PICUs. Our study findings suggested that patient age, unit size, and practice policy of physical restraint could be associated with more frequent use of physical restraints.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".