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Record W3015372504 · doi:10.1016/j.clinph.2020.04.005

A proposal for new diagnostic criteria for ALS

2020· article· de· W3015372504 on OpenAlexaff
Jeremy M. Shefner, Ammar Al‐Chalabi, Mark R. Baker, Liying Cui, Mamede de Carvalho, Andrew Eisen, Julian Großkreutz, Orla Hardiman, Robert D. Henderson, José Manuel Matamala, Hiroshi Mitsumoto, Walter J. Paulus, Neil G. Simon, Michael Swash, Kevin Talbot, Martin R. Turner, Yoshikazu Ugawa, Leonard H. van den Berg, Renato J. Verdugo, S. Vucic, Ryuji Kaji, David Burke, Matthew C. Kiernan

Bibliographic record

VenueClinical Neurophysiology · 2020
Typearticle
Languagede
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsUniversity of British Columbia
FundersMedical Research CouncilMotor Neurone Disease Association
KeywordsClinical neurophysiologyLicenseMedicinePolitical scienceMedical physicsLibrary scienceComputer sciencePsychiatryLaw

Abstract

fetched live from OpenAlex

Funding Information: The consensus conference upon which this manuscript is based was funded by: IFCN, World Federation of Neurology, ALS Association, and MND Association Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.056
metaresearch head score (Gemma)0.071
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.295

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.071
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0040.008
Bibliometrics0.0120.004
Science and technology studies0.0050.009
Scholarly communication0.0070.010
Open science0.0110.006
Research integrity0.0140.028
Insufficient payload (model declined to judge)0.0080.006

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 head0.195
GPT teacher head0.458
Teacher spread0.263 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations574
Published2020
Admission routes1
Has abstractyes

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