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Record W4386877716 · doi:10.1080/21678421.2023.2260832

Harmonized standard operating procedures for administering the ALS functional rating scale-revised

2023· article· en· W4386877716 on OpenAlexaff
Jeremy M. Shefner, Tommy M. Bunte, Gale Kittle, Angela Genge, Leonard H. van den Berg

Bibliographic record

VenueAmyotrophic Lateral Sclerosis and Frontotemporal Degeneration · 2023
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsRating scaleCertificationOutcome (game theory)Scale (ratio)Reliability engineeringPhysical medicine and rehabilitationPsychologyComputer scienceStatisticsMedicineMathematicsEngineeringCartography

Abstract

fetched live from OpenAlex

The ALS Functional Rating Scale-Revised is the most commonly used primary outcome measure in current ALS clinical trials. While rigorous training and certification is generally recognized as critical to reliable performance, differences have existed between training in the two groups responsible for most training in ALS outcome measures. We present a harmonized standard operating procedure which is intended to further reduce response variability by the use of identical training in North America and Europe.

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)0.104
metaresearch head score (Gemma)0.156
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: Methods · Consensus signal: Methods
Teacher disagreement score0.104
Threshold uncertainty score0.552

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1040.156
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.004
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0030.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0180.016

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 head0.088
GPT teacher head0.307
Teacher spread0.219 · 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
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

Citations12
Published2023
Admission routes1
Has abstractyes

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