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Record W1998042508 · doi:10.1038/ejhg.2014.95

Baraitser–Winter cerebrofrontofacial syndrome: delineation of the spectrum in 42 cases

2014· article· en· W1998042508 on OpenAlexaff
Alain Verloès, Nataliya Di Donato, Julien Masliah‐Planchon, Marjolijn C.J. Jongmans, Omar A Abdul-Raman, Beate Albrecht, Judith Allanson, Han G. Brunner, Débora Romeo Bertola, Nicolas Chassaing, Albert David, Koenraad Devriendt, Pirayeh Eftekhari, Valérie Drouin‐Garraud, Francesca Faravelli, Laurence Faivre, Fabienne Giuliano, Leina Guion Almeida, Jorge L. Juncos, Marlies Kempers, Hatice Koçak Eker, Didier Lacombe, Angela E. Lin, Grazia M.S. Mancini, Daniela Melis, Charles Marques Lourenço, Victoria Mok Siu, G Morin, Marjan M. Nezarati, Małgorzata J.M. Nowaczyk, Jeanette C. Ramer, Sara Osimani, Nicole Philip, Mary Ella Pierpont, Vincent Procaccio, Zeichi-Seide Roseli, Massimiliano Rossi, Cristina Rusu, Yves Sznajer, Ludivine Templin, Vera Uliana, Mirjam Klaus, Bregje W.M. van Bon, Conny van Ravenswaaij, Bruce H. Wainer, Andrew E. Fry, Andreas Rump, Alexander Hoischen, Séverine Drunat, Jean‐Baptiste Rivière, William B. Dobyns, Daniela T. Pilz

Bibliographic record

VenueEuropean Journal of Human Genetics · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsMcMaster UniversityNorth York General HospitalWestern UniversityChildren's Hospital of Eastern Ontario
FundersBaily Thomas Charitable FundNational Institute for Social Care and Health Research
KeywordsMicrocephalyHypertelorismMedicinePtosisLissencephalyPachygyriaPathologyAnatomyBiologyGeneticsPediatricsSurgery

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 score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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

Citations174
Published2014
Admission routes1
Has abstractno

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