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Record W4285393965 · doi:10.3390/children9071042

Early Breast Milk Volumes and Response to Galactogogue Treatment

2022· article· en· W4285393965 on OpenAlexafffund
Elizabeth Asztalos, Alex Kiss

Bibliographic record

VenueChildren · 2022
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersCanadian Institutes of Health Research
KeywordsDomperidoneMedicineLactationBreast milkRandomizationAnimal scienceBreastfeedingRandomized controlled trialInternal medicinePediatricsPregnancyChemistryBiology

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate the effect of galactogogue management in mothers of very preterm infants with varying breast milk volumes prior to initiating this treatment. Data were utilized from 90 women who participated in a trial employing domperidone. Three groups were formed according to their breast milk volumes (based on their infants’ birth weight) at the time of randomization and study entry to the trial protocol: (1) ≤100 mL/kg/d; (2) 101–200 mL/kg/d; and (3) ≥201 mL/kg/d. Breast milk volumes were evaluated at the 14- and 28-day study treatment periods. All three groups showed a significant volume increase and volume percent increase both at the 14-day measure and also the 28-day measure. Mothers who started in the two lower volume groups showed the greatest % volume change overall, with 356.2% in the ≤100 mL/kg/d and 106.1% in the 101–200 mL/kg/d groups, compared to those mothers in the higher group of ≥201 mL/kg/d, showing a change of 45.2%, where p = 0.001. Mothers producing varying low volumes were able to demonstrate an effect from the use of domperidone and increase their volumes as much as three-hundred-fold over 14- and 28-day study periods. However, those mothers whose volumes were ≤100 mL/kg/d continued to maintain low absolute milk volumes, putting these mothers at ongoing risk of ceasing lactation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.262
Teacher spread0.251 · 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.

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

Citations4
Published2022
Admission routes2
Has abstractyes

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