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Record W4416135042 · doi:10.1007/s12311-025-01928-6

Correction: The Natural History Study and Biomarker Collection of the Clinical Research Consortium for the Study of Cerebellar Ataxia (CRC-SCA)

2025· article· en· W4416135042 on OpenAlexaff
Yi-Cheng Lin, Nadia Amokrane, Sandie Worley, Lauren R. Moore, Andrew Rosen, Laura P. Crespo, Kelsey Trace, Tetsuo Ashizawa, Andrew Billnitzer, Susan B. Perlman, Aaron J. Fisher, Khalaf Bushara, Michael D. Geschwind, Cameron Dietiker, Christopher M. Gomez, Mahesh Padmanaban, Puneet Opal, Rizwan Akhtar, Henry L. Paulson, Sharan R. Srinivasan, Amy Ferng, Frank D. Ferrari, Chiadi U. Onyike, Ann Fishman, Sarah H. Ying, Ashley Paul, Jeremy D. Schmahmann, Christopher D. Stephen, Anoopum S. Gupta, Chih-Chun Lin, S. H. Subramony, Matthew R. Burns, George Wilmot, Antoine Duquette, Theresa Zesiewicz, Marie Y. Davis, Ali G. Hamedani, Joaquín A. Vizcarra, Stefan M. Pulst, Sharon Primeaux, Christian Rummey, Gülin Öz, Vikram G. Shakkottai, Liana S. Rosenthal, Sheng‐Han Kuo

Bibliographic record

VenueThe Cerebellum · 2025
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersNational Institutes of Health
KeywordsNatural historyNatural history studyBiomarkerClinical researchClinical neurologyNeurologyCerebellar ataxiaBiomarker discovery

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)0.013
metaresearch head score (Gemma)0.226
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: Other · Consensus signal: none
Teacher disagreement score0.069
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.226
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.004
Science and technology studies0.0030.003
Scholarly communication0.0050.003
Open science0.0060.003
Research integrity0.0100.012
Insufficient payload (model declined to judge)0.0690.026

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 head0.137
GPT teacher head0.390
Teacher spread0.253 · 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
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractno

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