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Record W3016532672 · doi:10.1038/s41436-020-0787-4

Resolving the dark matter of ABCA4 for 1054 Stargardt disease probands through integrated genomics and transcriptomics

2020· article· en· W3016532672 on OpenAlexaff
Mubeen Khan, Stéphanie S. Cornelis, Laura Whelan, Esmee H. Runhart, Ketan Mishra, Femke Bults, Yahya AlSwaiti, Alaa AlTalbishi, Sandro Banfi, Eyal Banin, Miriam Bauwens, Tamar Ben‐Yosef, Camiel J. F. Boon, L. Ingeborgh van den Born, Sabine Defoort, Aurore Devos, Adrian Dockery, Ľubica Ďuďáková, Ana Fakin, G. Jane Farrar, Juliana Maria Ferraz Sallum, Kaoru Fujinami, Christian Gilissen, Damjan Glavač, Michael B. Gorin, Jacquie Greenberg, Takaaki Hayashi, Ymkje M. Hettinga, Alexander Hoischen, Carel B. Hoyng, Karsten Hufendiek, Herbert Jägle, Smaragda Kamakari, Marianthi Karali, Ulrich Kellner, Caroline C. W. Klaver, Bohdan Kousal, Tina M. Lamey, Ian M. MacDonald, Anna Matynia, Terri L. McLaren, Marcela Mena, Isabelle Meunier, Rianne Miller, Hadas Newman, Buhle Ntozini, Monika Ołdak, Marc Pieterse, Osvaldo L. Podhajcer, Bernard Puech, Raj Ramesar, Klaus Rüther, Manar Salameh, Mariana Vallim Salles, Dror Sharon, Francesca Simonelli, Georg Spital, Marloes Steehouwer, Jacek P. Szaflik, Jennifer A. Thompson, C. Thuillier, Anna M. Tracewska, Martine van Zweeden, Andrea L. Vincent, Xavier Zanlonghi, Petra Lišková, Heidi Stöhr, John N. De Roach, Carmen Ayuso, Lisa Roberts, Bernhard H. F. Weber, Claire‐Marie Dhaenens, Frans P.M. Cremers

Bibliographic record

VenueGenetics in Medicine · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsUniversity of Alberta
FundersEngineer Research and Development CenterBundesministerium für Bildung und ForschungEuropean Regional Development FundLandelijke Stichting voor Blinden en SlechtziendenInstituto de Salud Carlos IIIStichting BlindenhulpMedical Research CouncilFundación Conchita RábagoFoundation Fighting BlindnessVlaamse regeringStichting tot Verbetering van het Lot der BlindenFondazione RomaUniverzita Karlova v PrazeSouth African Medical Research CouncilUniversity of WashingtonMinistero dell’Istruzione, dell’Università e della RicercaMedical Research Charities GroupBoehringer Ingelheim FondsStichting Steunfonds UitzichtFonds Wetenschappelijk OnderzoekStichting Blinden-PenningAlgemene Nederlandse Vereniging ter voorkoming van BlindheidUniversiteit GentMinistry of Health, State of Israel
KeywordsABCA4Stargardt diseaseProbandGenomicsGeneticsDiseaseBiologyComputational biologyMedicineMutationGenomeInternal medicineGene

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.020
GPT teacher head0.259
Teacher spread0.239 · 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

Citations142
Published2020
Admission routes1
Has abstractno

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