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Record W2089211913 · doi:10.4081/pr.2014.5534

Music Benefits on Postoperative Distress and Pain in Pediatric Day Care Surgery

2014· article· en· W2089211913 on OpenAlexaff
Valeria Calcaterra, Selene Ostuni, Irene Bonomelli, Simonetta Mencherini, Marco Brunero, Elisa Zambaiti, Savina Mannarino, Daniela Larizza, Riccardo Albertini, Carmine Tinelli, Glória Pelizzo

Bibliographic record

VenuePediatric Reports · 2014
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsFLACC scaleMedicineMusic therapyBlood pressureDistressAnesthesiaHeart ratePerioperativeRhythmPain scalePhysical therapyAnxietyPostoperative painInternal medicine

Abstract

fetched live from OpenAlex

Postoperative effect of music listening has not been established in pediatric age. Response on postoperative distress and pain in pediatric day care surgery has been evaluated. Forty-two children were enrolled. Patients were randomly assigned to the music-group (music intervention during awakening period) or the non-music group (standard postoperative care). Slow and fast classical music and pauses were recorded and played via ambient speakers. Heart rate, blood pressure, oxygen saturation, glucose and cortisol levels, faces pain scale and Face, Legs, Activity, Cry, Consolability (FLACC) Pain Scale were considered as indicators of response to stress and pain experience. Music during awakening induced lower increase of systolic and diastolic blood pressure levels. The non-music group showed progressive increasing values of glycemia; in music-group the curve of glycemia presented a plateau pattern (P<0.001). Positive impact on reactions to pain was noted using the FLACC scale. Music improves cardiovascular parameters, stress-induced hyperglycemia. Amelioration on pain perception is more evident in older children. Positive effects seems to be achieved by the alternation of fast, slow rhythms and pauses even in pediatric age.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.117
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.024
GPT teacher head0.283
Teacher spread0.259 · 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

Citations45
Published2014
Admission routes1
Has abstractyes

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