Multiple Sclerosis Clinic Utilization is Associated with Fewer Emergency Department Visits
Bibliographic record
Abstract
OBJECTIVE: Alberta is a Canadian province with a high prevalence of multiple sclerosis (MS). In this ecological study, we examined group differences in health care utilization among persons with MS (pwMS) living within different regions of the province. METHODS: pwMS were identified from provincial administrative databases spanning 2002-2011. Utilization of health care services was determined for a 2-year period (April 2010-March 2012). Residential postal codes placed patients into their provincial health care zones. As data were provided to the investigators in an aggregated form, tests of statistical significance and confounding were not performed. RESULTS: In total, 11,721 pwMS were identified. During the 2-year observation period, 96.2% of pwMS accessed a family physician and 57.1% accessed a neurologist. Nearly all (99.0%) pwMS who received neurologist care in Calgary visited an MS clinic, in contrast to Edmonton where a larger proportion (34.8%) received solely community neurologist care. More pwMS living in Edmonton accessed the ED (41.1%) compared to Calgary (35.7%), and the rate of visits per pwMS was higher in Edmonton (1.07/pwMS) than in Calgary (0.81/pwMS). The frequency of inpatient admissions was similar. CONCLUSIONS: Over 2 years, most pwMS accessed primary care and over half saw a neurologist. Despite a similar frequency of inpatient admissions, the frequency of ED visits by pwMS was higher in Edmonton compared to Calgary, where more patients received MS clinic care. Although this exploratory study is subject to several limitations, our findings suggest that specialized MS clinics may reduce costly ED visits.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".