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Record W2889031480 · doi:10.7717/peerj.5407

Addressing cultural, racial and ethnic discrepancies in guideline discordant gestational weight gain: a systematic review and meta-analysis

2018· review· en· W2889031480 on OpenAlexafffund
Kathryn M. Denize, Nina Acharya, Stéphanie A. Prince, Danilo Fernandes da Silva, Alysha L. J. Dingwall‐Harvey, Zachary M. Ferraro, Kristi B. Adamo

Bibliographic record

VenuePeerJ · 2018
Typereview
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsMedicineEthnic groupMeta-analysisWeight gainObservational studyGuidelinePregnancyDemographySystematic reviewFamily medicineCohort studyMEDLINEGerontologyBody weightInternal medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To systematically review the literature and describe the discrepancies in achieving the 2009 Institute of Medicine (IOM) gestational weight gain (GWG) guidelines across cultures. METHODS: Ten databases were searched from inception to April 2018. Observational cohort studies were included that examined adult women; reported on a measure of culture; compared cultural groups, and reported on GWG. Articles were broken down into papers that used the current 2009 IOM GWG guidelines and those that used others. A meta-analysis was conducted for studies using the 2009 guidelines examining the prevalence of discordant GWG across cultural groups. RESULTS: The review included 86 studies. Overall, 69% of women experienced discordant GWG irrespective of culture. White women experienced excessive GWG most often, and significantly more than Asian and Hispanic women; Black women had a higher prevalence of excessive GWG than Hispanic and Asian women; however, this difference was not significant. CONCLUSIONS: The majority of women experience excessive GWG, with White women experiencing this most often. Culturally diverse GWG guidelines are needed to individualize antenatal care and promote optimal maternal-fetal health outcomes across cultural groups.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.901
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
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 head0.327
GPT teacher head0.494
Teacher spread0.167 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations22
Published2018
Admission routes2
Has abstractyes

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