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Record W3136300643 · doi:10.1093/jpepsy/jsab028

Children’s Pain During IV Induction: A Randomized-Controlled Trial With the MEDi® Robot

2021· article· en· W3136300643 on OpenAlexaff
Rachelle Lee-Krueger, Jacqueline Reynolds Pearson, Adam Spencer, Mélanie Noël, Lisa Bell-Graham, Tanya Beran

Bibliographic record

VenueJournal of Pediatric Psychology · 2021
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsAlberta Children's HospitalHotchkiss Brain InstituteAlberta Health ServicesAlberta HealthUniversity of Calgary
Fundersnot available
KeywordsRandomized controlled trialMedicineCoping (psychology)Odds ratioPhysical therapyAnesthesiaClinical psychologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: This study examined the impact of a humanoid robot (MEDi®) programmed to teach deep breathing as a coping strategy, on children's pain and fear as primary and secondary outcomes, respectively, during intravenous (IV) line placement. The completion of IV induction was also examined as an exploratory outcome. METHODS: In this randomized controlled, two-armed trial, 137 children (4-12 years) were recruited in Short Stay Surgery at a tertiary pediatric hospital. Patients were randomly assigned to standard care (SC) with Ametop© only (N = 60) or SC and robot-facilitated intervention (N = 59) before induction. Pain and fear before, during, and after IV insertion were rated by patients and observers. RESULTS: No significant differences were found between groups and there were no changes over time for pain or fear (ps > .05). Exploratory analyses show that patients in the MEDi® group were 5.04 times more likely to complete IV induction, compared to SC, Fisher's exact test: X2 (1) = 4.85, p = .04, φc = 0.22, odds ratio = 5.04, 95% CI [1.06, 24.00]. CONCLUSION: This study was the first to examine children's IV induction experience when provided MEDi® support. Reasons for nonsignificance, limitations, and research suggestions were made.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.044
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.293
Teacher spread0.281 · 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 teacher head, 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

Citations16
Published2021
Admission routes1
Has abstractyes

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