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Record W4387866172 · doi:10.1080/01942638.2023.2271064

Interventions to Enhance Achievement to Independent Oral Feeds in Premature Infants: A Scoping Review

2023· review· en· W4387866172 on OpenAlexaff
Samiira Omar Sheikh-Mohamed, Hillary Wilson, Sandra Fucile

Bibliographic record

VenuePhysical & Occupational Therapy In Pediatrics · 2023
Typereview
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychological interventionMedicinePsychologyIntensive care medicineNursing

Abstract

fetched live from OpenAlex

AIM: To assess the effectiveness of interventions aimed at facilitating the transition from full tube to independent oral feeds in premature infants. METHODS: Scoping review methodology using the Preferred Reporting items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA_ScR). A search of six databases (EMBASE, MEDLINE, CINAHL, Web of Science, COCHRANE, and OT Seeker), using keywords related to oral feeding and premature infants retrieved 11,870 articles. Full-text screening was completed for 36 articles, and 21 articles were included in this review. RESULTS: = 1). Oral stimulation had the most studies with consistent evidence supporting its beneficial effect to facilitate achievement to independent oral feeds, swallow/gustatory stimulation appeared to have some benefit, but evidence for olfactory, tactile/kinesthetic, and auditory stimulation was sparse. CONCLUSION: Oral stimulation has the most studies with consistent evidence, and thus is suggested as a suitable early intervention strategy that can be used by health providers to facilitate the achievement to independent oral feeds in premature infants. The alternate forms of stimulation have limited evidence and necessitate further studies to confirm their benefits.

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)0.008
metaresearch head score (Gemma)0.027
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 score0.008
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.027
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.008
Bibliometrics0.0070.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.110
GPT teacher head0.477
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 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

Citations4
Published2023
Admission routes1
Has abstractyes

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