Comorbidities Are Associated with Altered Health Services Use in Multiple Sclerosis: A Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Persons with multiple sclerosis (MS) use health resources with greater frequency than the general population. However, little is known regarding which patient characteristics might contribute. OBJECTIVE: The study aimed to evaluate characteristics associated with healthcare use in MS patients. METHODS: Consecutive MS clinic attendees were recruited (September-November 2010), with clinical, demographic, and patient-completed questionnaires collected at 3 visits over 2 years. Linkage with administrative data (hospital, physician, and pharmacy records) provided healthcare use outcomes until December 31, 2013. Findings were reported as adjusted rate ratios (adjRRs) using negative binomial regression. RESULTS: A total of 340 MS patients with a mean (SD) age of 48.4 (12.0) years and subsequent follow-up of 3.1 (0.34) years were included. Fatigue and high physical comorbidity count (≥3 vs. none) were significantly associated with higher rates of physician encounters (adjRRs: 1.37 and 1.52, respectively), prescriptions filled (adjRRs: 1.25 and 1.40), and hospitalizations (adjRRs: 4.02 and 3.45). In addition, anxiety, disruptive pain, and perceived functional cognitive difficulties were associated with higher rates of physician encounters and prescriptions dispensed (adjRR ranged from 1.28 to 1.48). DISCUSSION: The presence of fatigue and higher physical comorbidity burden were associated with higher rates of health services use. Findings have implications for those examining healthcare burden or organizing health services for persons with MS.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.000 |
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.
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".