Multiple Sclerosis Disease-Modifying Therapy Prescribing Patterns in Ontario
Bibliographic record
Abstract
BACKGROUND: Differences in Multiple sclerosis (MS) disease-modifying therapy (DMT) prescribing patterns between different groups of neurologists have not been explored. OBJECTIVE: To examine concentrations of prescribing patterns and to assess if MS-specialists use a broader range of DMTs relative to general neurologists. METHODS: We conducted a cross-sectional study using administrative claims databases in Ontario, Canada to link neurologists to 2009 DMT prescription data. MS specialization was defined using both practice location and prescription patterns. Lorenz curves and Gini coefficients were constructed to examine prescribing patterns, separating neurologist characteristics dichotomously and separating Avonex from the other standard DMTs (Betaseron, Rebif and Copaxone). Gini coefficient 95% confidence intervals (CIs) were derived using jack-knife statistical techniques. RESULTS: Prescriptions were highly concentrated with 12% of Ontario neurologists prescribing 80% of DMTs. There was a trend towards Avonex being more commonly prescribed relative to the other DMTs. When MS specialization was defined by DMT prescribing, high-volume prescribing neurologists showed a broader range of DMT prescribing (Gini 0.38-0.44) in comparison to low-volume prescribers (Gini 0.57-0.66). CONCLUSIONS: The majority of DMTs are prescribed by a small subset of neurologists. High-volume prescribing MS-specialists show more variability in DMT use while low-volume prescribers tend to individually focus on a narrower range of DMTs.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".