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Record W2385316123 · doi:10.1016/j.kint.2016.01.033

Controversies and research agenda in nephropathic cystinosis: conclusions from a “Kidney Disease: Improving Global Outcomes” (KDIGO) Controversies Conference

2016· article· en· W2385316123 on OpenAlexaff
Craig B. Langman, Bruce A. Barshop, Georges Deschênes, Francesco Emma, Paul Goodyer, Graham Lipkin, Julian Midgley, Chris Ottolenghi, Aude Servais, Neveen A. Soliman, Jess G. Thoene, Elena Levtchenko, Oliver Amon, Gema Ariceta, Maryan Basurto, Leticia Belmont‐Martínez, Aurélia Bertholet‐Thomas, Marjolein Bos, Thomas Brown, Stéphanie Cherqui, Elisabeth A. M. Cornelissen, Monte A. Del Monte, Jie Ding, Ranjan Dohil, Maya Doyle, Ewa Elenberg, William A. Gahl, Víctor Aguado Gómez, Marcella Greco, Christy Greeley, Larry A. Greenbaum, Paul C. Grimm, Katharina Hohenfellner, Teresa M. Holm, Valerie Hotz, Mirian C. H. Janssen, Frederick J. Kaskel, Rita Magriço, Galina Nesterova, Philip Newsholme, Patrick Niaudet, Patrice Rioux, Minnie Sarwal, Jerry A. Schneider, Rezan Topaloğlu, Doris A. Trauner, María Helena Vaisbich, Lambertus P. van den Heuvel, William van’t Hoff

Bibliographic record

VenueKidney International · 2016
Typearticle
Languageen
FieldMedicine
TopicBiomedical Research and Pathophysiology
Canadian institutionsAlberta Children's HospitalMcGill UniversityMontreal Children's Hospital
Fundersnot available
KeywordsCystinosisCysteamineMedicineDiseaseKidney diseaseFanconi syndromeIntensive care medicinePediatricsCystineKidneyInternal 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)0.095
metaresearch head score (Gemma)0.113
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.095
Threshold uncertainty score0.501

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0950.113
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0050.006
Science and technology studies0.0050.012
Scholarly communication0.0170.019
Open science0.0050.012
Research integrity0.0250.032
Insufficient payload (model declined to judge)0.0090.002

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 head0.046
GPT teacher head0.371
Teacher spread0.325 · 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
GenreCommentary

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

Citations83
Published2016
Admission routes1
Has abstractno

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