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Record W2947472942 · doi:10.1093/pch/pxz066.112

113 Humanoid robot-based distraction to reduce pain and distress during venipuncture in the pediatric emergency department: A randomized controlled trial

2019· article· en· W2947472942 on OpenAlexaff
Robin Manaloor, Samina Ali, Keon Ma, Mithra Sivakumar, Ben Vandermeer, Tanya Beran, Shannon D. Scott, Timothy A.D. Graham, Sarah Curtis, Hsing Jou, Lisa Hartling

Bibliographic record

VenuePaediatrics & Child Health · 2019
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsAlberta Health ServicesUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsVenipunctureDistractionEmergency departmentDistressMedicineRandomized controlled trialPhysical therapyPhysical medicine and rehabilitationPsychologyAnesthesiaSurgeryNursingClinical psychology

Abstract

fetched live from OpenAlex

Intravenous insertion (IVI) is identified by children as extremely painful and the resultant distress can have lasting negative consequences. There is an urgent need to effectively manage such procedures. Our primary objective was to compare the pain and distress of IVI with the addition of humanoid robot-based distraction to standard care, versus standard care alone. This two-armed randomized controlled trial was conducted from April 2017 to May 2018. Due to the nature of the intervention, it was not possible to blind participants, research assistants, or emergency department staff. The biostatistician was blind to treatment allocation. Children received standard medical care and those in the distraction group received the additional intervention (robot), along with standard care. Children aged 6 to 11 years who required IVI were included. Exclusion criteria included hearing or visual impairments, neurocognitive delays, sensory impairment to pain, previous enrollment, and discretion of the ED clinical staff. A total of 426 pediatric patients were screened and 340 were excluded. We recruited 86 children, of which 55% (47/86) were male; 9% (7/82) were premature at birth; 82% (67/82) had a previous ED visit; 30% (25/82) required previous hospitalization; 78% (64/82) had previous IV placement and 96% (78/81) received topical anesthesia. A statistically significant reduction in distress was observed with the addition of robot-based distraction to standard care. The mean total distress score measured via the Observational Scale of Behavioral Distress (OSBD-R) was 1.49±2.36 (standard care) compared to 0.78±1.32 (robot group) (p=0.047). The median Faces Pain Score (FPS-R) during the IV procedure was 4 (IQR 2,6) in the standard care group alone, compared to 2 (IQR 0,4) with the addition of humanoid robot-based distraction (p=0.10). Change in parental state anxiety pre-procedure versus post-procedure was not significantly different between groups (p=0.49). Parental satisfaction with the IV start was 93% (39/42) in the robot arm compared to 74% (29/39) in the standard care arm (p=0.03). Parents were also more satisfied with management of their child’s pain in the robot group (95% very satisfied) compared with standard care (72% very satisfied) (p=0.002). Humanoid robot-based distraction therapy reduces distress and to a lesser extent, pain, in children undergoing IVI in the ED. Further trials are required to confirm utility in other age groups and settings.

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)0.002
metaresearch head score (Gemma)0.004
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: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.009
GPT teacher head0.285
Teacher spread0.276 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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