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Record W3176130873 · doi:10.31083/j.ceog.2021.03.2413

Gestational weight gain and long-term postpartum weight retention

2021· article· en· W3176130873 on OpenAlexaff
Alexandra Berezowsky, Howard Berger

Bibliographic record

VenueClinical and Experimental Obstetrics & Gynecology · 2021
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsWeight gainMedicinePregnancyObstetricsScopusObesityWeight lossGestational ageWeight changeMEDLINEBody weightInternal medicine

Abstract

fetched live from OpenAlex

Background: Excessive gestational weight gain is related to postpartum weight retention and multiple short- and long-term adverse outcomes. These include pregnancy related complications as preeclampsia and higher rates of cesarean delivery and long-term morbidities as future obesity and metabolic syndrome. Even so, more than half of the pregnant women gain excessive weight during their pregnancy. Methods: This review included a database search of Medline, ClinicalKey, PubMed, PubMed Central, Scopus, Ovid, and the Cochrane Database of Systemic Reviews. We included original articles, systematic reviews and meta-analysis published in peer-reviewed journals between January 1990 and October 2020 that addressed the correlation between excessive gestational weight gain, postpartum weight retention and maternal health issues. Only articles published in the English language that were available at full length, were included in this review. Results and discussion: After reviewing the literature, we discuss the risk factors for excessive gestational weight gain, the association between excessive gestational weight gain and postpartum weight retention and the implications of excessive gestational weight gain on women’s future health. Finally, we highlight future research opportunities related to these issues.

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)0.002
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 score0.045
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.044
GPT teacher head0.362
Teacher spread0.318 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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