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Record W4405808375 · doi:10.1053/j.ajkd.2024.10.009

Clinical Spectrum and Prognosis of Atypical Autosomal Dominant Polycystic Kidney Disease Caused by Monoallelic Pathogenic Variants of IFT140

2024· article· en· W4405808375 on OpenAlexfundno aff
Nikola Zagorec, Alizée Calamel, Eric Olinger, Sarah Orr, John A. Sayer, Vignesh-Guru Pillay, Anne‐Sophie Denommé‐Pichon, Frédéric Tran Mau‐Them, Sophie Nambot, Laurence Faivre, Elisabet Ars, Roser Torrá, Albert Ong, Olivier Devuyst, Aurore Després, Hugo Lemoine, Jonathan de Fallois, R. Brousse, Aurélie Hummel, Bertrand Knebelmann, Nathalie Maisonneuve, Jan Halbritter, Yannick Le Meur, Marie‐Pierre Audrézet, Émilie Cornec-Le Gall

Bibliographic record

VenueAmerican Journal of Kidney Diseases · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Kidney Cyst Diseases
Canadian institutionsnot available
FundersCliniques Universitaires Saint-LucMedical Research CouncilEuropean Rare Kidney Disease Reference NetworkSheffield Teaching Hospitals NHS Foundation TrustCentre hospitalier universitaire Sainte-JustineKidney Research UKSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungWellcome TrustAgence Nationale de la RechercheNational Institute for Health and Care ResearchHeart of England NHS Foundation TrustCancer Research UKDeutsche ForschungsgemeinschaftCentre hospitalier régional universitaire de LilleUniversité Catholique de LouvainQueen Mary University of LondonUniversität Zürich
KeywordsMedicinePolycystic kidney diseaseDiseaseAutosomal dominant polycystic kidney diseaseKidney diseasePolycystic kidneyInternal medicinePathology

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.006
GPT teacher head0.264
Teacher spread0.258 · 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

Citations9
Published2024
Admission routes1
Has abstractno

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Same venueAmerican Journal of Kidney DiseasesSame topicGenetic and Kidney Cyst DiseasesFrench-language works237,207