General Practitioner Preferences in Managing Care of Multiple Sclerosis Patients
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is a lifelong neurological disorder requiring care in a variety of settings. The purpose of this study is to describe preferences of general practitioners (GPs) with regards to providing care for MS patients. METHODS: A stratified sample of 900 GPs in the province of Quebec were sent a questionnaire, with 266 returning completed questionnaires. Respondents were surveyed about their preferences using four clinical scenarios describing hypothetical patients experiencing different stages of MS. Respondents were asked whether they would continue managing the patient themselves, formally refer the patient to a specialist, or seek specialist advice. RESULTS: In two scenarios representing stable courses, 40.9% and 61.6% of GPs, respectively, intended to manage the patient themselves. GPs who reported having experience with MS patients were more likely to report an intention to continue management. In one scenario, GPs operating in rural areas were less likely to consider management than those in the Montreal metropolitan area (odds ratio=0.422, 95% confidence interval 0.20-0.90). CONCLUSIONS: For MS patients with a stable disease course, an important proportion of GPs appear to be willing to manage long-term care for MS patients.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".