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Record W7066855628

Interpregnancy Weight Change Among Mothers of a Child with a Major Congenital Anomaly: A Danish Nationwide Cohort Study

2022· article· en· W7066855628 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2022
Typearticle
Languageen
FieldComputer Science
TopicHistory of Computing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsDanishSick childPregnancyCohort studyCohortSingletonLow birth weightUniversity hospitalExcellence
DOInot available

Abstract

fetched live from OpenAlex

Eyal Cohen,1– 3 Péter Szentkúti,4 Erzsébet Horváth-Puhó,4 Hilary K Brown,3,5 Sonia M Grandi,2 Henrik Toft Sørensen,4,6 Joel G Ray2,3,7 1Department of Pediatrics and Edwin S.H. Leong Centre for Healthy Children, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada; 2Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada; 3Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada; 4Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; 5Department of Health & Society, University of Toronto Scarborough; 6Clinical Excellence Research Center, Stanford University, Stanford, CA, USA; 7St.Michael’s Hospital Department of Medicine, University of Toronto, Toronto, Ontario, CanadaCorrespondence: Eyal Cohen, Department of Pediatrics and Edwin S.H. Leong Centre for Healthy Children, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada, Tel +1 416-813-7654, Email eyal.cohen@sickkids.caBackground: The mother of an infant with a major congenital anomaly is at a higher risk of premature cardiometabolic disease, possibly from chronic caregiver stress and distraction from self-care, including maintaining a healthy lifestyle and body weight.Objective: To compare the interpregnancy weight gain in women whose first infant had a major congenital anomaly vs those without an affected child.Methods: Multivariable linear regression compared women whose infant had an anomaly vs those whose infant did not, adjusting for interpregnancy time interval, demographics, smoking and health status at the first pregnancy.Results: Of the 199,536 women who had two consecutive singleton births, 4035 (2.0%) had a child with an anomaly at the first birth. The mean (SD) maternal BMI at the start of the first pregnancy was 24.1 (4.7) and 23.7 (4.4) kg/m2 in women with, and without, an anomaly-affected newborn. By the start of the second pregnancy, 3 years later, they had gained a mean (SD) of 2.2 (5.5) and 1.8 (5.2) kg, respectively – an adjusted absolute higher gain of 0.26 kg (95% CI 0.10 to 0.42) in women with an anomaly-affected first-born infant compared to those with an unaffected pregnancy. The adjusted interpregnancy weight gain difference was greatest in women whose first-born infant had a multi-organ anomaly at 0.59 kg (95% CI 0.02 to 1.16). The adjusted odds ratio of moving from a normal BMI category of 18.5– 24.9 kg/m2 in the first pregnancy, to an overweight or obese BMI category of 25+ kg/m2 in the second, was 1.18 (95% CI 1.06 to 1.32) comparing mothers with vs without an anomaly-affected child in the first pregnancy.Conclusion: Mothers of an infant with a major congenital anomaly have a modestly higher interpregnancy weight gain and tend to climb to a higher BMI category. The long-term implications of this greater weight trajectory require further study.Keywords: weight gain, congenital anomaly, inter-pregnancy, body mass index

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)0.002
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 score0.097
Threshold uncertainty score0.193

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.098
GPT teacher head0.433
Teacher spread0.335 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueDOAJ (DOAJ: Directory of Open Access Journals)Same topicHistory of Computing TechnologiesFrench-language works237,207