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Alternative female kangaroo care for procedural pain in preterm neonates: a pilot study

2012· article· en· W1628176823 on OpenAlexafffund
Céleste Johnston, Jasmine Byron, Françoise Filion, Marsha Campbell‐Yeo, Sharyn Gibbins

Bibliographic record

VenueActa Paediatrica · 2012
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsSunnybrook Health Science CentreHealth Sciences CentreMcGill UniversityMcGill University Health CentreIzaak Walton Killam Health Centre
FundersCanadian Institutes of Health ResearchFonds de recherche du Québec
KeywordsMedicineKangaroo careHeelGestational ageCrossover studyRandomized controlled trialObstetricsPediatricsPregnancySurgery

Abstract

fetched live from OpenAlex

AIM: To determine the feasibility and effect size of kangaroo care (KC) for pain from heel lance in preterm neonates provided by either the infant's mother (MKC) or an unrelated alternate female (AFKC). METHODS: Using a randomized crossover design, preterm neonates (n = 18) between 28 and 37 weeks gestational age within 10 days of life from two university-affiliated level III NICU's undergoing routine heel lance were assigned to receive KC 30 min before and during the procedure from either their mother (MKC) or an unrelated woman. In the second heel lance procedure at least 24 h later but within 10 days, the infants were crossed over to the other condition. RESULTS: There was a 48% participation rate, with only 40 of 82 eligible cases having maternal consent. The main reason for refusal was discomfort with another woman providing kangaroo care. The effect sizes on the pain scores (PIPP) were small, ranging from .23 to .43 across the first 2 min of procedure. CONCLUSION: The difference between nonrelated females and the mother in decreasing pain response is small, although not negligible. Given the high refusal rate, nonrelated females are a less desirable alternative to mothers than fathers.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.029
GPT teacher head0.305
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 teacher head, not a consensus.

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

Citations30
Published2012
Admission routes2
Has abstractyes

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