Cold and vibration for children undergoing needle‐related procedures: A non‐inferiority randomized clinical trial
Bibliographic record
Abstract
Abstract The use of a rapid, easy‐to‐use intervention could improve needle‐related procedural pain management practices in the context of the Emergency Department (ED). As such, the Buzzy device seems to be a promising alternative to topical anesthetics. The aim of this study was to determine if a cold vibrating device was non‐inferior to a topical anesthetic cream for pain management in children undergoing needle‐related procedures in the ED. In this randomized controlled non‐inferiority trial, we enrolled children between 4 and 17 years presenting to the ED and requiring a needle‐related procedure. Participants were randomly assigned to either the cold vibrating device or topical anesthetic (4% liposomal lidocaine; standard of care). The primary outcome was the mean difference (MD) in adjusted procedural pain intensity on the 0–10 Color Analogue Scale (CAS), using a non‐inferiority margin of 0.70. A total of 352 participants were randomized (cold vibration device n = 176, topical anesthetic cream n = 176). Adjusted procedural pain scores' MD between groups was 0.56 (95% CI:−0.08–1.20) on the CAS, showing that the cold vibrating device was not considered non‐inferior to topical anesthetic. The cold vibrating device was not considered non‐inferior to the topical anesthetic cream for pain management in children during a needle‐related procedure in the ED. As topical anesthetic creams require an application time of 30 min, cost approximately CAD $40.00 per tube, are underused in the ED setting, the cold vibrating device remains a promising alternative as it is a rapid, easy‐to‐use, and reusable device.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".