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Record W4399140013 · doi:10.1038/s41582-024-00978-4

Publisher Correction: The Miami Framework for ALS and related neurodegenerative disorders: an integrated view of phenotype and biology

2024· review· en· W4399140013 on OpenAlexaff
Michael Benatar, Joanne Wuu, Edward D. Huey, Corey T. McMillan, Ronald B. Postuma, Caroline McHutchison, Laynie Dratch, Jalayne J. Arias, Anita Crawley, Henry Houlden, Michael McDermott, Xueya Cai, Neil Thakur, Adam L. Boxer, Howard J. Rosen, Bradley F. Boeve, Penny A. Dacks, Stephanie Cosentino, Sharon Abrahams, Neil A. Shneider, Paul Lingor, Jeremy M. Shefner, Peter M. Andersen, Ammar Al‐Chalabi, Martin R. Turner, Peggy Allred, Stanley H. Appel, David Benatar, James D. Berry, Meg Bradbury, Lucie Bruijn, Jennifer Buczyner, Nathan Carberry, James B. Caress, Thomas H. Champney, Kuldip D. Dave, Stephanie Fradette, Volkan Granit, Anne‐Laure Grignon, Amelie K. Gubitz, Matthew B. Harms, Terry Heiman‐Patterson, Sharon Hesterlee, Karen A. Lawrence, Travis “Pete” Lewis, Oren Levy, Tahseen Mozaffar, Christine Stanislaw, Alexander G. Thompson, Olga Uspenskaya, Patrick Weydt, Lorne Zinman

Bibliographic record

VenueNature Reviews Neurology · 2024
Typereview
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsUniversity of TorontoMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute of Mental HealthNational Institute on Aging
KeywordsMiamiPhenotypeBiologyNeuroscienceCognitive scienceComputational biologyPsychologyGeneticsGene

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)0.007
metaresearch head score (Gemma)0.043
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: Other · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.043
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.003
Science and technology studies0.0020.004
Scholarly communication0.0050.004
Open science0.0050.003
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0310.025

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 head0.075
GPT teacher head0.421
Teacher spread0.346 · 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
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

Citations2
Published2024
Admission routes1
Has abstractno

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