Variability across versions of the self-administered ALSFRS-R: a review and call for harmonization
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease predominantly affecting motor neurons resulting in substantial, progressive disability. The amyotrophic lateral sclerosis functional rating scale - revised (ALSFRS-R) is commonly used to assess and monitor functional status in patients with ALS. Additionally, it is the current regulatory accepted primary outcome measure documenting functional status in ALS clinical trials. The ALSFRS-R was originally designed to be administered to a patient by a trained professional. But over time it has been adapted to be performed independently by patients or their caregivers without assistance. Several different versions of the self-administered ALSFRS-R have been created over the past two decades, each with subtle but important differences. Some of these differences are related to language used in item wording or the platform for which the scale was intended to be administered (e.g. digitally). These differences across versions of the self-administered scale may be problematic as they could increase the heterogeneity of data collected across clinical trials or complicate interpretation of results across trials. Therefore, we highlight the need for a harmonized version of the self-administered ALSFRS-R to be used across all clinics and clinical trial sites internationally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".