Development of a General Composite Scale (GENCOMS) for Progressive Neurodegenerative Diseases and Implications for the Assessment of Disease-Modifying Therapies
Bibliographic record
Abstract
INTRODUCTION: The reliable assessment of treatment outcomes for disease-modifying therapies (DMT) in neurodegenerative disease is challenging. The objective of this paper is to describe a generalized framework for developing composite scales that can be applied in diverse, degenerative conditions, termed "GENCOMS." Composite scales optimize the sensitivity for detecting clinically meaningful effects that slow disease progression. METHODS: The GENCOMS method relies on robust natural history data and/or placebo arm data from DMT trials. Validated scales that are core to the disease process have been identified, and item level data obtained to standardize the response outcomes from 0 (best possible score) to 1 (worst possible score). A partial least squares regression analysis was conducted with temporal change as the dependent variable and change scores in standardized items as the explanatory variables. The derived model coefficients constitute a weighted sum of items that most effectively measure disease progression. RESULTS: The resultant composite scale was optimized to detect disease progression and can be examined in a range of slow or fast progressing populations. The scale can be used in studies with comparable patient populations as an endpoint optimized to measure disease progression and therefore ideally suited to assess treatment effects in DMTs. CONCLUSION: The methodology presented here provides a generalizable framework for developing composite scales in the assessment of neurodegenerative disease progression and evaluation of DMT effects. By objectively selecting and weighting items from previously validated measures based solely on their sensitivity to disease progression, this methodology allows for the creation of a more responsive measurement of clinical decline. This heightened sensitivity to clinical decline can be utilized to detect modest yet meaningful treatment effects in the early stages of neurogenerative diseases, when it is optimal to begin a DMT.
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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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".