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Record W3109617315 · doi:10.17863/cam.59417

External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis

2020· article· en· W3109617315 on OpenAlexafffund
Kym I E Snell, John Allotey, Melanie Smuk, Richard Hooper, Claire Chan, Asif Ahmed, Lucy C. Chappell, Peter von Dadelszen, Marcus Green, Louise C. Kenny, Asma Khalil, Khalid S. Khan, Ben W. Mol, Jenny Myers, Lucilla Poston, B. Thilaganathan, Anne C. Staff, Gordon C. S. Smith, Wessel Ganzevoort, Hannele Laivuori, Anthony Odibo, J. Arenas Ramírez, John‏ Kingdom, Diane Farrar, Ahmet Baschat, Paul T. Seed, Federico Prefumo, Fabrício da Silva Costa, Henk Groen, François Audibert, Jacques Massé, Ragnhild Bergene Skråstad, Kjell Å. Salvesen, Camilla Haavaldsen, Chie Nagata, Alice Rumbold, Seppo Heinonen, Lisa Askie, Luc Smits, Christina Anne Vinter, Per Magnus, Eero Kajantie, Pia Villa, Anne Karen Jenum, Louise Bjørkholt Andersen, Jane E. Norman, Akihide Ohkuchi, Anne Eskild, Sohinee Bhattacharya, Fionnuala M. McAuliffe, Alberto Galindo, Ignacio Herraı̀z, Lionel Carbillon, Kerstin Klipstein‐Grobusch, Seon Ae Yeo, Joyce L. Browne, Karel G.M. Moons, Richard D Riley, Shakila Thangaratinam

Bibliographic record

VenueUniversity of Southern Denmark Research Portal (University of Southern Denmark) · 2020
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsUniversité LavalUniversité de MontréalUniversity of Toronto
FundersNIHR School for Primary Care ResearchHealth Technology Assessment ProgrammeMichigan State UniversitySyddansk UniversitetJohns Hopkins UniversityCollege of Engineering, Michigan State UniversityUniversitetet i OsloUniversité de MontréalNational and Kapodistrian University of AthensDepartment of Health and Social CareMedical Research CouncilKementerian Pendidikan NasionalNorwegian Institute of Public HealthAcademisch Medisch CentrumUniversity College DublinUniversity of BristolNational Institute for Health and Care ResearchUniversidad de los AndesUniversity of DundeeUniversity of OxfordNorges Teknisk-Naturvitenskapelige UniversitetUniversity of AberdeenUniversité LavalUniversiteit MaastrichtUniversity of Toronto
KeywordsEclampsiaCalibrationPredictive modellingConfidence intervalStatisticStatisticsMedicineMeta-analysisPrediction intervalPregnancyMathematicsInternal 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)0.086
metaresearch head score (Gemma)0.143
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.455

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0860.143
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0100.032
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0040.002
Open science0.0030.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.287
GPT teacher head0.328
Teacher spread0.041 · 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 designMeta-analysis
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
Published2020
Admission routes2
Has abstractno

Explore more

Same venueUniversity of Southern Denmark Research Portal (University of Southern Denmark)Same topicPregnancy and preeclampsia studiesFrench-language works237,207