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Record W2194463649 · doi:10.1177/1753495x15591320

Gestational weight gain and medical outcomes of pregnancy

2015· review· en· W2194463649 on OpenAlexafffund
Zachary M. Ferraro, Fernanda Contador, Afaf Tawfiq, Kristi B. Adamo, Laura Gaudet

Bibliographic record

VenueObstetric Medicine · 2015
Typereview
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsUniversity of OttawaOttawa HospitalChildren's Hospital of Eastern Ontario
FundersInstitute of Human Development, Child and Youth HealthCanadian Institutes of Health Research
KeywordsMedicineWeight gainPregnancyOffspringObstetricsNarrative reviewGestationPsychological interventionIntensive care medicineBody weightPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

This narrative review discusses gestational weight gain (GWG) and medical outcomes of pregnancy, including metabolic, cardiovascular, respiratory, musculoskeletal and psychiatric systems. Taken as a whole, the available evidence shows that excessive GWG increases the risk of all medical complications of pregnancy, and negatively impacts the long-term health and weight of both mothers and their offspring. Briefly, interventions to encourage appropriate GWG are discussed and readers are directed to resources to facilitate discussion of pregnancy weight.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.085
GPT teacher head0.399
Teacher spread0.314 · 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 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

Citations60
Published2015
Admission routes2
Has abstractyes

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