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Record W4415233426 · doi:10.17615/x4hy-s163

The Impact of Excluding Adverse Neonatal Outcomes on the Creation of Gestational Weight Gain Charts Among Women from Low- and Middle-income Countries with Normal and Overweight BMI

2025· article· en· W4415233426 on OpenAlexfundno aff
Holger W. Unger, Carl Lachat, Kenneth Maleta, Hamid Jan Jan Mohamed, Hawawu Hussein, Nancy F. Krebs, Stephen J. Rogerson, Dominique Roberfroid, Patricia Lima Rodrigues, Peter Clayton, Malay Kanti Mridha, Preetha Ramachandra, K. Michael Hambidge, Jennifer A. Hutcheon, Kathryn G. Dewey, J. CRUICKSHANK, Reyna Sámano, Zulfiqar A Bhutta, Marly Augusto Cardoso, Paola Soledad Mosquera, Wafaie Fawzi, Jacqueline M Lauer, Omolola Ayoola, Joanne Katz, Maria Ome‐Kaius, James M Tielsch, Austrida Gondwe, Shibani Ghosh, Cinthya Muñoz‐Manrique, Chunrong Zhong, Subarna K. Khatry, Thaís Rangel Bousquet Carrilho, Frankie Fair, Alida Melse‐Boonstra, Marhazlina Mohamad, Hora Soltani, Carla Adriane Leal de Araújo, Hugo Martínez‐Rojano, Susana L Matias, Elizabeth M. McClure, See Ling Loy, Shams El Arifeen, Patrícia Helen de Carvalho Rondó, Bhim P Shrestha, Robin Shrestha, Delanjathan Devakumar, Shalean M. Collins, Andrew M. Prentice, Charles Mangani, Gabriela Chico‐Barba, Seth Adu‐Afarwuah, Tinku Thomas, Fereidoun Azizi, Henrik Friis, Yemane Berhane, Sajid Soofi, Anura V. Kurpad, Rinaldo Artes, Romaina Iqbal, Teija Kulmala, members of the GWG Pooling Project Consortium, Per Ashorn, Patrick Kolsteren, Pratibha Dwarkanath, Adam Bawa Yussif, Robin M. Bernstein, Juliana dos Santos Vaz, Yves Martin‐Prével, Sophie E. Moore, Reynaldo Martorell, Nur Indrawaty Lipoeto, Laura Beatríz López, Lieven Huybregts, Anna Lartey, Dongqing Wang, Elvira Beatriz Calvo, David Osrin, Barnabas Natamba, Ameyalli M. Rodríguez-Cano, Maíra Barreto Malta, Lingxia Zeng, Nianhong Yang, Manfred Accrombessi, Molin Wang, Naomi Saville, Siddharudha Shivalli, Dayana Rodrigues Farias, Guadalupe Estrada‐Gutiérrez, Alemayehu Worku, Anthony Costello, Valérie Briand, José Roberto da Silva, Samira Behboudi‐Gandevani, Yue Cheng, Qian Li, Nega Assefa, João Guilherme Bezerra Alves, Ahmed Tijani Bawah, Lotta Hallamaa, Amy Girard, Zhonghai Zhu, Exnevia Gomo, Otilia Perichart‐Perera, Gilberto Kac, Shama Munim, Dharma Manandhar, Usha Ramakrishnan, Joshua D. Miller, Sera L. Young

Bibliographic record

VenueUNC Libraries · 2025
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsnot available
FundersMichael Smith Health Research BCBill and Melinda Gates Foundation
KeywordsOverweightPercentileWeight gainBirth weightNormal weightGestational ageGestationPregnancy

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)0.017
metaresearch head score (Gemma)0.166
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 score0.083
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.166
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.250
Teacher spread0.238 · 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

Citations0
Published2025
Admission routes1
Has abstractno

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