Clinical Profile of Persons with Multiple Sclerosis Across the Continuum of Care
Bibliographic record
Abstract
BACKGROUND: This study is part of the Innovations in Data, Evidence and Applications for Persons with Neurological Conditions project to understand the strengths, preferences, and needs of persons with neurological conditions living in Canada. OBJECTIVE: To estimate the prevalence and describe the sociodemographic and clinical characteristics of persons with multiple sclerosis in Canadian home care, nursing home, Complex Continuing Care hospitals, and inpatient mental health care settings. METHODS: Cross-sectional study of adults aged 18 years and older with multiple sclerosis (MS; n=11,250) across Canada from 1996 through 2011 using interRAI Resident Assessment Instrument (RAI) comprehensive health assessments (RAI Minimum Data Set 2.0, RAI-Home Care, RAI-Mental Health). Comparisons were made to adults with Alzheimer's disease and related dementias (n=260,910), other neurological conditions (n=163,578) and non-neurological conditions (n=571,567). RESULTS: The prevalence of MS was highest in Complex Continuing Care hospitals (4125 cases per 100,000 patients), followed by home care (2020 cases per 100,000 patients), nursing homes (1424 cases per 100,000 patients), and mental health settings (138 cases per 100,000 patients). Persons with MS experienced greater impairment in the completion of activities of daily living, pain, pressure ulcers, swallowing difficulty, depression, and anxiety compared with peers within care settings. There were also significant differences between settings, particularly the degree of physical and cognitive impairment experienced by persons with MS. CONCLUSIONS: Except for mental health care settings, the prevalence of MS in community, institutional and hospital-based care settings exceeded that of the general population. These data describing the sociodemographic and clinical characteristics of persons with MS may be used to inform clinical practice and policy decisions for persons with MS across the continuum of care.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".