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Record W2738486023 · doi:10.1111/apa.14002

Magnetic noninvasive acupuncture for infant comfort (<scp>MAGNIFIC</scp>) – a single‐blinded randomised controlled pilot trial

2017· article· en· W2738486023 on OpenAlexaff
Kerry L. Chen, Kwee Bee Lindrea, Im Quah‐Smith, Georg M. Schmölzer, Mary B. Daly, Tim Schindler, Ju Lee Oei

Bibliographic record

VenueActa Paediatrica · 2017
Typearticle
Languageen
FieldMedicine
TopicAcupuncture Treatment Research Studies
Canadian institutionsRoyal Alexandra HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineHeelPlaceboAcupunctureAnalgesicAnesthesiaRandomized controlled trialPain scoreSingle blindPhysical therapySurgery

Abstract

fetched live from OpenAlex

AIM: To determine the safety and feasibility of auricular noninvasive magnetic acupuncture (MA) to decrease infant pain during heel pricks. METHODS: Infants requiring heel pricks for blood collection were randomised to either MA (n = 21) or placebo (P) (n = 19) after parental informed consent. MA or placebo stickers were placed on both ears according to the Battlefield Protocol by an unblinded investigator and left on for 3 days. Pain was assessed with the Premature Infant Pain Profile (PIPP) by blinded clinicians. RESULTS: Mean gestation (MA:34.1, P:34.4 weeks) and age of infants (MA:5.3, P:4.5 days) were similar as were mean (SD) pre (MA:1.7(1.4), P:2.1(1.9)) and post (MA:1.6(1.4), P: 2.1(1.7)) heel prick PIPP scores. PIPP scores were significantly lower in MA infants during heel pricks (MA:5.9(3.7), P: 8.3(4.7), p = 0.04). One-way ANCOVA modelling showed that MA was significantly associated with lower PIPP scores after controlling for analgesic use (p = 0.043). No differences in heart rate, oxygen saturation, analgesic use or adverse effects (e.g. local skin reactions) noted. CONCLUSION: This pilot study shows that auricular MA is feasible in neonates and may reduce PIPP scores during heel pricks. Further study is required to determine the impact of MA on other painful or stressful conditions and on neurodevelopment.

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)0.001
metaresearch head score (Gemma)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
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 score0.156
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.046
GPT teacher head0.319
Teacher spread0.274 · 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.

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

Citations33
Published2017
Admission routes1
Has abstractyes

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