A population-based study comparing multiple sclerosis clinic users and non-users in British Columbia, Canada
Bibliographic record
Abstract
ABSTRACTObjectivesMuch clinical knowledge about multiple sclerosis (MS) has been gained from patients who attend MS specialty clinics. However, there is limited information about whether these patients are representative of the wider MS population. The objective of this study was to compare incident MS cases who were MS clinic users to non-users of the specialty MS clinics in British Columbia, Canada. ApproachThis was a retrospective record linkage cohort study using prospectively collected data from the BCMS database and province-wide health administrative databases. Incident MS cases were identified in the general population using a validated algorithm of hospital and physician diagnostic codes. Results There were 2,841 incident MS cases between 1996 and 2004 including 1,648 (58%) that had registered at an MS clinic (‘clinic cases’) and 1,193 (42%) that had not (‘non-clinic cases’). Sex and socioeconomic status distributions were similar; however, non-clinic cases were older, accessed health services more frequently, and had a higher burden of comorbidity than clinic cases. Only 1% of the non-clinic cases had filled a prescription for an MS-specific disease-modifying therapy, compared to 51% of the clinic cases. ConclusionOur findings have several important implications: even within a publicly funded healthcare system, a high proportion of individuals with MS may not access a specialty MS clinic; the needs of MS patients managed in the community may differ from those referred to an MS clinic; findings from studies involving clinic-based MS cohorts may not always be generalizable to the wider MS population; and access to population-based health administrative data offers the opportunity to gain a broader understanding of 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".