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Record W2794010984

Abstract 16939: Tube-assisted Feeding is Associated With Worse Longitudinal Growth in Children With Single Ventricle Cardiac Disease

2016· article· en· W2794010984 on OpenAlexaff
Arene Butto, Laura Mercer‐Rosa, Carrie Daymont, Jonathan B. Edelson, Erika Mejia, Meryl S. Cohen

Bibliographic record

VenueCirculation · 2016
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineVentricleCohortRetrospective cohort studyCardiac surgeryWeight gainHeart diseasePediatricsHypoplastic left heart syndromeDiseaseInternal medicineSurgeryCardiologyBody weight
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Children with single ventricle cardiac disease (SVCD) have poor growth in early life. Tube-assisted feeding (TF) has increasingly been used to improve weight gain in SVCD patients, but little is known about its long-term effects on growth. We sought to compare the longitudinal growth of SVCD patients receiving TF at the time of hospital discharge after initial cardiac surgery with those fed entirely by mouth. Methods: We conducted a retrospective cohort study of patients who underwent initial surgical palliation for SVCD from 1999 to 2009, excluding premature infants ( Results: A total of 135 patients met inclusion criteria; 64% were male and 50% had HLHS. There were 44 patients (33%) in the TF group. Median weight z-score was significantly lower in the TF group at each year of life from ages 2 to 6 years (p Conclusions: Long-term growth of children with SVCD who require TF at hospital discharge is diminished compared to that of peers who are fed entirely by mouth. TF patients with SVCD do not experience catch-up growth during early childhood.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.244
Teacher spread0.223 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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