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Record W4224306863 · doi:10.1016/j.ajog.2022.04.019

Effects of prenatal exposure to maternal COVID-19 and perinatal care on neonatal outcome: results from the INTERCOVID Multinational Cohort Study

2022· article· en· W4224306863 on OpenAlexaff
Francesca Giuliani, D. Orós, Robert B. Gunier, Sonia Deantoni, Stephen Rauch, Roberto Casale, Ricardo Nieto, Enrico Bertino, Albertina Rego, Camilla Menis, Michael G. Gravett, Massimo Candiani, Philippe Deruelle, Perla K. García-May, Mohak Mhatre, Mustapha Ado Usman, Sherief Abd‐Elsalam, Saturday Etuk, R. Napolitano, Becky Liu, Federico Prefumo, Valeria Savasi, Marynéa Silva do Vale, Eric Baafi, Shabina Ariff, Nerea Maíz, Muhammad Baffah Aminu, Jorge Arturo Cardona–Pérez, Rachel Craik, Gabriela Tavchioska, Babagana Bako, Caroline Benski, Fatimah Hassan-Hanga, Mónica Savorani, Loı̈c Sentilhes, Maria Carola Capelli, Ken Takahashi, Carmen Vecchiarelli, Satoru Ikenoue, Ramachandran Thiruvengadam, Constanza P. Soto Conti, Irene Cetin, Vincent Bizor Nachinab, Ernawati Ernawati, Eduardo Alfredo Duro, Kholin A.M. Kholin, Jagjit S. Teji, Sarah Rae Easter, Laurent Salomon, Adejumoke Idowu Ayede, Rosa Maria Cerbo, Josephine Agyeman-Duah, Paola Roggero, Brenda Eskenazi, Ana Langer, Zulfiqar A Bhutta, Stephen Kennedy, Aris T. Papageorghiou, José Villar

Bibliographic record

VenueAmerican Journal of Obstetrics and Gynecology · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsHospital for Sick Children
FundersGombe State UniversitySt George's University Hospitals NHS Foundation TrustUniversity College London Hospitals NHS Foundation TrustTranslational Health Science and Technology InstituteInstituto de Seguriidad y Servicios Sociales de los Trabadores del EstadoUniversidade Federal de Minas GeraisUniversitas AirlanggaGreen Templeton College, University of OxfordKeio UniversityUniversità degli Studi di BresciaJikei University School of MedicineUniversity of OxfordNational Institute for Health and Care ResearchUniversity of WashingtonUniversity College LondonUniversità degli Studi di TorinoTanta UniversityUniversità degli Studi di MilanoTufts Medical Center
KeywordsMedicinePregnancyObstetricsBreastfeedingFetal distressPediatricsGestational diabetesGestational ageCohortCohort studyPreeclampsiaPrenatal careVaginal deliveryFetusGestationPopulationInternal 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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.309
Teacher spread0.296 · 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

Citations76
Published2022
Admission routes1
Has abstractno

Explore more

Same venueAmerican Journal of Obstetrics and GynecologySame topicCOVID-19 Impact on ReproductionFrench-language works237,207