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Record W4213125336 · doi:10.3390/children9020276

Association of Weight Changes by Three Days after Birth and Mortality and/or Severe Neurological Injury in Preterm Infants < 29 Weeks Gestational Age: A Multicenter Cohort Study

2022· article· en· W4213125336 on OpenAlexaffabout
Carlos Zozaya, Khalid Aziz, Nalini Singhal, Xiang Y. Ye, Christine Drolet, Julie Emberley, Kyong‐Soon Lee, Vibhuti Shah

Bibliographic record

VenueChildren · 2022
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsHospital for Sick ChildrenJaneway Children's Health and Rehabilitation CentreMemorial University of NewfoundlandUniversity of AlbertaUniversité LavalCentre hospitalier universitaire de QuébecUniversity of CalgaryMount Sinai Hospital
Fundersnot available
KeywordsMedicineGestational ageConfoundingBirth weightCohort studyPediatricsLow birth weightRetrospective cohort studyCohortWeight changeObstetricsPregnancyWeight lossSurgeryInternal medicineObesity

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aimed to determine the range of weight loss, at 3 days postnatal age, associated with the lowest risk of mortality/short-term morbidity in preterm infants <29 weeks gestational age (GA). STUDY DESIGN: This multicenter retrospective cohort study employed data from the Canadian Neonatal Network database. The primary outcome was a composite of mortality and/or severe neurological injury. Multivariable quadratic and linear regression models which adjusted for potential confounders were built. RESULTS: A total of 9275 preterm infants (median GA 26, IQR 25, 28 weeks) were included. The optimal weight change range at day three, after adjustment for potential confounders for the primary outcomes, was -15 to -8.9%. CONCLUSIONS: There is a 'U'-shaped relationship between weight change from birth to day three and mortality and/or severe neurological injury. Interventional studies, which target weight loss within the range found in this study and evaluate the impact on neonatal outcomes, are needed to corroborate our findings.

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 score0.002
Threshold uncertainty score0.455

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 head0.011
GPT teacher head0.255
Teacher spread0.244 · 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

Citations13
Published2022
Admission routes2
Has abstractyes

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