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Record W4312161073 · doi:10.1016/j.ajcnut.2022.11.021

Gestational weight gain according to the Brazilian charts and its association with maternal and infant adverse outcomes

2022· article· en· W4312161073 on OpenAlexaff
Thaís Rangel Bousquet Carrilho, Jennifer A. Hutcheon, Kathleen M. Rasmussen, Michael Eduardo Reichenheim, Dayana Rodrigues Farias, Nathalia Freitas-Costa, Gilberto Kac, Adauto Emmerich Oliveira, Ana Paula Esteves‐Pereira, Ana Paula Sayuri Sato, Antônio Augusto Moura da Sílva, Caroline de Barros Gomes, Cláudia Leite de Moraes, Cláudia Saunders, Daniela da Silva Rocha, Denise Cavalcante de Barros, Denise Petrucci Gigante, Edson Theodoro dos Santos Neto, Elisa Maria de Aquino Lacerda, Elizabeth Fujimori, Fernanda Garanhani Surita, Isabel Oliveira Bierhals, Jane de Carlos Santana Capelli, José Guilherme Cecatti, Juliana dos Santos Vaz, Juraci Almeida César, Marco Fábio Mastroeni, Maria Antonieta de Barros Leite Carvalhães, María do Carmo Leal, Marlos Rodrigues Domingues, Mayra Pacheco Fernandes, Michele Drehmer, Mônica de Araújo Batalha, Patrícia de Carvalho Padilha, Renato T. Souza, Silmara Salete de Barros Silva Mastroeni, Sílvia Regina Dias Médici Saldiva, Simone Seixas da Cruz, Sirlei Siani Moráis

Bibliographic record

VenueAmerican Journal of Clinical Nutrition · 2022
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWeight gainMedicineGestationObstetricsAssociation (psychology)Gestational ageSmall for gestational ageInfant mortalityPregnancyPediatricsEnvironmental healthBody weightPsychologyEndocrinologyPopulationBiology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.013
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 score0.102
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.352
Teacher spread0.333 · 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".

Quick stats

Citations10
Published2022
Admission routes1
Has abstractno

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