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Record W3110095855 · doi:10.1007/s10654-020-00697-2

Cohort profile: Singapore Preconception Study of Long-Term Maternal and Child Outcomes (S-PRESTO)

2020· article· en· W3110095855 on OpenAlexaff
Evelyn Xiu Ling Loo, Shu‐E Soh, See Ling Loy, Sharon Ng, Mya Thway Tint, Shiao‐Yng Chan, Jonathan Huang, Fabian Yap, Kok Hian Tan, Bernard Su Min Chern, Heng Hao Tan, Michael J. Meaney, Neerja Karnani, Keith M. Godfrey, Yung Seng Lee, Jerry Kok Yen Chan, Peter D. Gluckman, Yap Seng Chong, Lynette Pei‐Chi Shek, Johan G. Eriksson, Airu Chia, Anna Fogel, Anne Goh, Anne Chu, Anne Rifkin‐Graboi, Anqi Qiu, Bee Wah Lee, Bobby K. Cheon, Candida Vaz, Christiani Jeyakumar Henry, Ciarán G. Forde, Claudia Chi, Dawn X. P. Koh, Desiree Y. Phua, Doris Ngiuk Lan Loh, Elaine Phaik Ling Quah, Elizabeth Huiwen Tham, Evelyn Law, Faidon Magkos, G. S. H. Yeo, Hannah E. J. Yong, Helen Yu Chen, Hong Pan, Hugo P.S. Van Bever, Hui Min Tan, Izzuddin M. Aris, Jeannie Tay, Jia Xu, Joanne Yoong, Jonathan Tze Liang Choo, Jonathan Y. Bernard, Jun Shi Lai, Karen Tan, Kenneth Kwek, Keri McCrickerd, Kothandaraman Narasimhan, Kok Wee Chong, Kuan J. Lee, Li Chen, Lieng Hsi Ling, Ling‐Wei Chen, Lourdes Mary Daniel, Marielle V. Fortier, Mary Foong‐Fong Chong, Mei Chien Chua, Melvin Khee‐Shing Leow, Michelle Z. L. Kee, Min Gong, Navin Michael, Ngee Lek, Oon Hoe Teoh, Priti Mishra, Queenie Ling Jun Li, S. Sendhil Velan, Seng Bin Ang, Shirong Cai, Si Hui Goh, Sok Bee Lim, Stella Tsotsi, Stephen Chin-Ying Hsu, Sue‐Anne Toh, Suresh Anand Sadananthan, Teng Hong Tan, Tong Wei Yew, Varsha Gupta, Victor Samuel Rajadurai, Wee Meng Han, Wei Wei Pang, Wen Lun Yuan, Yanan Zhu, Yin Bun Cheung, Yiong Huak Chan, Zai Ru Cheng

Bibliographic record

VenueEuropean Journal of Epidemiology · 2020
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
FundersMedical Research CouncilNational University Health SystemNational Research FoundationPrecursory Research for Embryonic Science and TechnologyBritish Heart FoundationSingapore Institute for Clinical SciencesNational Institute for Health and Care ResearchNational Medical Research CouncilNational Research Foundation SingaporeDanoneMead Johnson Nutrition
KeywordsMedicineOffspringPregnancyCohortObstetricsCohort studyEpidemiologyGestationAnthropometryPediatricsProspective cohort studyInternal medicine

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)0.002
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 score0.052
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.091
GPT teacher head0.358
Teacher spread0.267 · 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

Citations90
Published2020
Admission routes1
Has abstractno

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