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Record W4392469923 · doi:10.1038/s41581-024-00818-0

Clinical practice recommendations for kidney involvement in tuberous sclerosis complex: a consensus statement by the ERKNet Working Group for Autosomal Dominant Structural Kidney Disorders and the ERA Genes & Kidney Working Group

2024· review· en· W4392469923 on OpenAlexafffund
Djalila Mekahli, Roman‐Ulrich Müller, Matko Marlais, Tanja Wlodkowski, Stefanie Haeberle, Marta López-Argumedo, Carsten Bergmann, Luc Breysem, Carla Fladrowski, Elizabeth P. Henske, Peter Janssens, François Jouret, J.C. Kingswood, Jean‐Baptiste Lattouf, Marc R. Liliën, Geert Maleux, Micaela Rozenberg, Stefan Siemer, Olivier Devuyst, Franz Schaefer, David J. Kwiatkowski, Olivier Rouvière, John J. Bissler

Bibliographic record

VenueNature Reviews Nephrology · 2024
Typereview
Languageen
FieldMedicine
TopicTuberous Sclerosis Complex Research
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersMedizinische Fakultät der Albert-Ludwigs-Universität FreiburgVlaamse regeringGIS-Institut des Maladies RaresUniversität des SaarlandesKU LeuvenUniversität ZürichFonds Wetenschappelijk OnderzoekBrigham and Women's HospitalAlbert-Ludwigs-Universität FreiburgUniversity College LondonUniversité Catholique de LouvainUniversität zu KölnUniversité de LiègeEuropean CommissionUniversité de MontréalUniversitätsklinikum KölnLam ResearchVrije Universiteit BrusselGreat Ormond Street Institute of Child HealthEuropean Rare Kidney Disease Reference NetworkEusko Jaurlaritza
KeywordsTuberous sclerosisMedicineKidneyPsychological interventionIntensive care medicineMultidisciplinary approachKidney diseasePathologyKidney disorderClinical PracticeBioinformaticsInternal medicinePsychiatryFamily medicineBiology

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)0.012
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0040.003
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0070.004

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 head0.164
GPT teacher head0.442
Teacher spread0.278 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations33
Published2024
Admission routes2
Has abstractno

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