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Record W2999602874 · doi:10.1080/03004430.2019.1707815

NICU positioning strategies to reduce stress in preterm infants: a scoping review

2020· review· en· W2999602874 on OpenAlexaff
Faith E. Wiley, Rebeccah Raphael, Parisa Ghanouni

Bibliographic record

VenueEarly Child Development and Care · 2020
Typereview
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSupine positionNeonatal intensive care unitPsychologyStress (linguistics)Stress reductionMedicinePhysical medicine and rehabilitationPediatricsPhysical therapySurgery

Abstract

fetched live from OpenAlex

Appropriate positioning maintains preterm infants in symmetrical posture and is important for stress reduction and impacting neurological or musculoskeletal development. This review was performed to elucidate which positioning methods emerge as the most beneficial. Several databases were searched using search terms for neonatal intensive care, preterm, infant positioning, and stress. Three hundred and fifty-eight articles were initially retrieved and nine were retained after applying inclusion criteria. Included studies varied widely in their methods and outcome measures. Prone positioning was supported by three studies, sidelying was found to reduce stress more than supine positioning, and supine positioning emerged as the least effective for controlling stress behaviours. Limited evidence supports the efficacy of alternative positioning techniques that contain infants in supine positions. Future studies should continue to investigate the impact of positioning on stress in order to optimize developmental outcomes for preterm infants.

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)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0060.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.322
Teacher spread0.298 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations14
Published2020
Admission routes1
Has abstractyes

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