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Record W2912904847 · doi:10.1177/1539449218825436

Pilot Testing a Robot for Reducing Pain in Hospitalized Preterm Infants

2019· article· en· W2912904847 on OpenAlexafffund
Nicholas Williams, Karon E. MacLean, Ling Guan, Jean Paul Collet, Liisa Holsti

Bibliographic record

VenueOTJR Occupational Therapy Journal of Research · 2019
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsB.C. Women's Hospital & Health CentreBC Children's HospitalWomen's Health Research InstituteUniversity of British Columbia
FundersCanadian Institutes of Health ResearchBC Children's HospitalFaculty of Medicine, University of British Columbia
KeywordsMedicineHeart rate variabilityAnesthesiaHeart rateRandomized controlled trialHeelSurgeryBlood pressureInternal medicine

Abstract

fetched live from OpenAlex

Optimizing neurodevelopment is a key goal of neonatal occupational therapy. In preterm infants, repeated procedural pain is associated with adverse effects on neurodevelopment long term. Calmer is a robot designed to reduce infant pain. The objective of this study was to examine the effects of Calmer on heart rate variability (HRV) during routine blood collection in preterm infants. In a randomized controlled pilot trial, 10 infants were assigned to either standard care ( n = 5, facilitated tucking [FT]) or Calmer treatment ( n = 5). HRV was recorded continuously and quantified using the area (power) of the spectrum in high and low frequency (HF: 0.15-0.40Hz/ms 2 ; LF: 0.04-0.15 Hz/ms 2 ) regions. Changes in HRV during three, 2-min phases (Baseline, Heel Poke, and Recovery) were compared between groups. Calmer infants had 90% greater parasympathetic activation ([PS] reduced stress) during Baseline, 82% greater PS activation during Poke, and 24% greater PS activation during Recovery than FT infants. Calmer reduced physiological preterm infant pain reactivity during blood collection.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.167
GPT teacher head0.432
Teacher spread0.266 · 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 designObservational
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

Citations49
Published2019
Admission routes2
Has abstractyes

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