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Record W2953132179 · doi:10.1097/pec.0000000000001867

Cold Vibration (Buzzy) Versus Anesthetic Patch (EMLA) for Pain Prevention During Cannulation in Children

2019· article· en· W2953132179 on OpenAlexaboutno aff
Stéphanie Bourdier, Nedjoua Khelif, Maria Velasquez, Alexandra Usclade, Emmanuelle Rochette, Brigitte Favard, Étienne Merlin, A. Labbé, Catherine Sarret, É. Michaud

Bibliographic record

VenuePediatric Emergency Care · 2019
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnesthesiaRandomizationLocal anestheticTopical anestheticEmergency departmentRandomized controlled trialPain scaleVisual analogue scalePain scoreSurgeryLidocainePhysical therapyNursing

Abstract

fetched live from OpenAlex

INTRODUCTION: The purpose of this study was to assess differences in observed pain-related behaviors during cannulation between a device combining cold and vibration (Buzzy) and the standard care (EMLA patch). METHODS: Patients 18 months to 6 years old, requiring venous access in a pediatric emergency department, received either the Buzzy device or the EMLA patch. Predefined week randomization ensured equal allocation to the 2 intervention groups. Pain during cannulation was measured using the Children's Hospital of Eastern Ontario Pain Scale. Parent and nurse reports, cannulation success, and venous access times were also assessed. RESULTS: In total, 607 included patients were randomized into the Buzzy group (n = 302) or the EMLA group (n = 305). Observed pain-related behaviors scores, parent-assessed pain scores, and nurse-reported pain ratings were higher with Buzzy. CONCLUSIONS: Pain relief by a combination of cold and vibration during cannulation is not as effective as the standard-care method in children 18 months to 6 years old.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.009
GPT teacher head0.270
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations35
Published2019
Admission routes1
Has abstractyes

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