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Record W2128488012 · doi:10.1177/1054773812469223

Intervention Minimizing Preterm Infants’ Exposure to NICU Light and Noise

2012· article· en· W2128488012 on OpenAlexafffund
Marilyn Aita, Céleste Johnston, Céline Goulet, Tim F. Oberlander, Laurie Snider

Bibliographic record

VenueClinical Nursing Research · 2012
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsUniversity of British ColumbiaMcGill UniversityUniversité de Montréal
FundersCanadian Institutes of Health ResearchCanadian Nurses FoundationSick Kids FoundationMcGill University
KeywordsMedicineNeonatal intensive care unitGestational ageConfoundingHeart rateHeart rate variabilityOxygen saturationPediatricsIntensive careRandomized controlled trialPregnancyIntensive care medicineBlood pressureInternal medicine

Abstract

fetched live from OpenAlex

Neonatal intensive care unit (NICU) light and noise may be stressful to preterm infants. This research evaluated the physiological stability of 54 infants born at 28- to 32-weeks' gestational age while wearing eye goggles and earmuffs for a 4-hour period in the NICU. Infants were recruited from four NICUs of university-affiliated hospitals and randomized to the intervention-control or control-intervention sequences. Heart rate (HR), heart rate variability (HRV), and oxygen saturation (O2 sat) were collected using the SomtéTM device. Confounding variables such as position and handling were assessed by videotaping infants during the study periods. Results indicated that infants had more stress responses while wearing eye goggles and earmuffs since maximum HR was found to be significantly higher and high-frequency power of HRV significantly lower during the intervention as compared with the control period. Therefore, this intervention is not recommended for the clinical practice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.111
GPT teacher head0.476
Teacher spread0.366 · 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 source (direct Gemma or distilled Codex), 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

Citations69
Published2012
Admission routes2
Has abstractyes

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