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Record W4392132400 · doi:10.1080/21678421.2024.2320880

The ALSFRS-R Summit: a global call to action on the use of the ALSFRS-R in ALS clinical trials

2024· article· de· W4392132400 on OpenAlexaff
Angela Genge, Jesse M. Cedarbaum, Jeremy M. Shefner, Adriano Chiò, Ammar Al‐Chalabi, Philip Van Damme, Christopher McDermott, Jonathan D. Glass, James D. Berry, Ruben P. A. van Eijk, Christina Fournier, Julian Großkreutz, Jinsy Andrews, Vanessa Bertone, Tommy M. Bunte, Mathias Couillard, Gale Kittle, John Polzer, Kristiana Salmon, Corey Straub, Leonard H. van den Berg

Bibliographic record

VenueAmyotrophic Lateral Sclerosis and Frontotemporal Degeneration · 2024
Typearticle
Languagede
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute for Health and Care Research
KeywordsClinical trialRating scaleScale (ratio)SummitMedical physicsMedicinePhysical therapyPsychologyInternal medicine

Abstract

fetched live from OpenAlex

The Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS) was developed more than 25 years ago as an instrument to monitor functional change over time in patients with ALS. It has since been revised and extended to meet the needs of high data quality in ALS trials (ALSFRS-R), however a full re-validation of the scale was not completed. Despite this, the scale has remained a primary outcome measure in clinical trials. We convened a group of clinical trialists to discuss and explore opportunities to improve the scale and propose alternative measures. In this meeting report, we present a call to action on the use of the ALSFRS-Revised scale in clinical trials, focusing on the need for (1) harmonization of the ALSFRS-R administration globally, (2) alignment on a set of recommendations for clinical trial design and statistical analysis plans (SAPs), and (3) use of additional outcome measures.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.397
GPT teacher head0.429
Teacher spread0.032 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations23
Published2024
Admission routes1
Has abstractyes

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