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Record W2101817002 · doi:10.12968/bjom.2015.23.5.323

Gastro-oesophageal reflux in the neonate: Clinical complexities and impact on midwifery practice

2015· article· en· W2101817002 on OpenAlexaff
Alex Mitchell, Kathryn Lamb, Ruth A. Sanders

Bibliographic record

VenueBritish Journal of Midwifery · 2015
Typearticle
Languageen
FieldMedicine
TopicGastroesophageal reflux and treatments
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsGastro-MedicineNursingRefluxObstetricsMultidisciplinary teamLimitingIntervention (counseling)NormalityCLARITYMultidisciplinary approachDiseasePsychiatry

Abstract

fetched live from OpenAlex

Gastro-oesophageal reflux (GOR) is a common neonatal issue seen by midwives, which can develop into a complex clinical picture when symptoms give rise to gastro-oesophageal reflux disease (GORD), requiring further intervention and multidisciplinary team working. This article discusses the differences between GOR and GORD from a midwifery stance, highlighting the importance of effective communication with parents, and within the wider health-care professions. Early midwifery recognition and symptom clarity for both GOR and GORD are explored with management strategies and treatment options for both issues considered. As frontline practitioners during the puerperium, midwives are centrally placed to offer care and advice, emphasising the normality and self-limiting nature of GOR in the neonate and providing reassurance to parents. The importance of a meticulous feeding assessment and holistic midwifery approach to neonatal and maternal wellbeing is also examined. In light of the recently published national guidance, the care provision for babies experiencing GOR and GORD necessitates further midwifery consideration to ensure family-centred care.

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)0.006
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0010.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.080
GPT teacher head0.409
Teacher spread0.329 · 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 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
Published2015
Admission routes1
Has abstractyes

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