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Record W3104221383 · doi:10.1089/bfm.2020.0110

Impact of Nipple Shield Use on Milk Transfer and Maternal Nipple Pain

2020· article· en· W3104221383 on OpenAlexaboutno aff
Viviane Silva Coentro, Sharon L. Perrella, Ching Tat Lai, Alethea Rea, Kevin Murray, Donna T. Geddes

Bibliographic record

VenueBreastfeeding Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBreastfeedingShieldVisual analogue scaleAnesthesiaPediatrics

Abstract

fetched live from OpenAlex

Background: Nipple pain is a common cause of early cessation of breastfeeding. A nipple shield (shield) is often used to improve breastfeeding comfort. There are concerns that shield use may limit milk transfer. The aims of this study were to determine whether shield use reduces milk transfer and maternal nipple pain. Methods: A within-subject study of two groups of breastfeeding dyads (infants <6 months) was conducted; Control Group (CG): no breastfeeding difficulties; Pain Group (PG) shield used for nipple pain. There were two monitored sessions where shield use was randomized. Test weights and pain questionnaires were completed, and percentage of available milk removed (PAMR) was calculated. Results: Twenty-five PG (6 ± 4 postnatal weeks) and 34 CG (9 ± 6 postnatal weeks) had similar 24-hour milk production (PG: 676 ± 239 mL, CG: 775 ± 162 mL, p = 0.083). PG mean milk transfer volume and PAMR did not differ with shield use (no shield: 46 mL, 59%; shield: 40 mL, 53%, volume p = 0.38, PAMR p = 0.64). CG mean volume and PAMR were reduced with shield use (no shield: 65 mL, 64%; shield: 31 mL, 33%, volume p < 0.001, PAMR p < 0.001). PG pain scores were similar with and without shield use (Visual Analog Scale p = 0.44, McGill p = 0.97). Conclusions: Shield use did not impact either milk production or milk transfer in breastfeeding women experiencing nipple pain.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.151
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.050
GPT teacher head0.306
Teacher spread0.256 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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