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Record W4200302999 · doi:10.3390/jcm10245871

Effectiveness of Alberta Family-Integrated Care on Neonatal Outcomes: A Cluster Randomized Controlled Trial

2021· article· en· W4200302999 on OpenAlexafffundabout
Madeleine Murphy, Vibhuti Shah, Karen Benzies

Bibliographic record

VenueJournal of Clinical Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsUniversity of CalgaryMount Sinai Hospital
FundersAlberta InnovatesAlberta Innovates - Technology Futures
KeywordsMedicineBreastfeedingNeonatal intensive care unitRandomized controlled trialPediatricsBirth weightGestational ageWeight gainLow birth weightIntensive careCluster randomised controlled trialCluster (spacecraft)PregnancyIntensive care medicineBody weightSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Family-Integrated Care (FICare) empowers parents to play an active role as a caregiver for their infant in the neonatal intensive care unit (NICU). This model of care is associated with improved neonatal outcomes, such as improved weight gain and higher breastfeeding rates at discharge in infants admitted to level III NICUs; however, its effectiveness in level II NICUs remains unproven. The objective of this study was to evaluate the effectiveness of the model on neonatal outcomes in a cluster randomized controlled trial conducted in 10 level II NICUs randomized to Alberta FICare or standard care. Mothers and their preterm infants born between 32+0 and 34+6 weeks’ gestational age were included. The primary outcome was the proportion of infants who regained their birth weight (BW) after 14 days of life. The analysis included 353 infants/308 mothers at Alberta FICare sites and 365 infants/306 mothers at standard care sites. There was no difference in the proportion of infants who had regained their BW by 14 days between the groups. A lack of perceived improved weight gain trajectory for those in the FICare group is attributed to a shorter length of hospital stay and infants being discharged prior to regaining BW.

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)0.008
metaresearch head score (Gemma)0.050
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.050
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.382
Teacher spread0.354 · 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 designRandomized trial
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

Citations34
Published2021
Admission routes3
Has abstractyes

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