The Impact of Spasticity Severity on Healthcare Utilization Among MS Patients: A Large-Scale Six-Year Follow-Up Study (P4.150)
Bibliographic record
Abstract
OBJECTIVE: To assess the association of spasticity and healthcare utilization (HCU) among North American Research Committee on Multiple Sclerosis (NARCOMS) registry participants. BACKGROUND: Over 80% of multiple sclerosis (MS) patients ultimately experience spasticity. Spasticity is associated with fatigue, pain, impaired mobility and lower quality of life. The impact of spasticity on HCU is poorly understood. DESIGN/METHODS: NARCOMS maintains a self-report database of MS patients who volunteer health-related information through online or paper questionnaires at enrollment and semi-annually thereafter. Among 5172 individuals who completed surveys in 2005 and 2011, we evaluated the association of HCU with spasticity reported using the 5-point Performance Scales spasticity subscale (PSss). RESULTS: Responders were predominantly female (76.2%) and white (96.2%). In 2005 their mean (SD) age was 52 (9.8) years, disease duration 13.7 (9.1) years, and 50.8% were able to walk unassisted. 18.8% reported no spasticity, 36.2% minimal, 17.5% mild, 15.7% moderate, 9.9% severe and 1.9% total spasticity. In the prior 6 months, 653 (13.2%) reported an ER visit while 358 (7.1%) reported a hospitalization. Frequencies for both increased with spasticity severity (p<0.001 for linear trend). Over 6 years, PSss improved or remained stable in 74.9% and worsened in 25.9%. As compared to participants whose PSss score worsened, those with improved spasticity were less likely to report an ER visit (OR 0.83; 95%CI: 0.67-1.03) or a hospitalization (OR 0.76; 95%CI: 0.59-0.99) even after accounting for disability and sociodemographic factors. CONCLUSIONS: More severe and worsening spasticity is associated with greater HCU, emphasizing the adverse impact of spasticity. Study Supported by: NARCOMS is supported in part by the Consortium of Multiple Sclerosis Centers (CMSC) and this study by Allergan, Inc.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".