New applications for independent activities of daily living in measuring disability in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Disability outcome measures in multiple sclerosis (MS) focus heavily on ambulation; however, limitations in performing everyday activities encompass another type of disability. OBJECTIVES: The aim of this study was to examine the ability of instrumental activities of daily living (IADL) scale to discriminate between different levels of disability and to predict disability progression. METHODS: The North American Research Committee on Multiple Sclerosis (NARCOMS) registry fall 2006 semi-annual survey asked participants to complete the RAND-12, Performance Scales, Patient Determined Disease Steps (PDDS), and IADL questionnaires. We modeled the trajectory of disability change, using the PDDS, over 12 years. Analyses used linear and repeated measures regression methods. RESULTS: = 9931), 9559 (96%) completed the PDDS and IADL scale. Respondents were mostly female (76%), Caucasian (92%), and 52.3 (10.5) years old with moderate disability (median PDDS 4 (early cane)). Mean (SD) IADL total score was 20.5 (3.7). Discriminant ability of the IADL scale was higher than other measures considered at higher levels of disability. Adjusted longitudinal models showed that needing greater assistance on IADLs was independently predictive of trajectories of greater disability change. CONCLUSION: IADL scale had a greater ability to discriminate between higher disability levels than RAND-12 domains. The IADL scale may provide a useful and clinically relevant tool to measure disability in progressive MS populations.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".