P.027 Multiple Sclerosis Self-reflective Treatment Evaluation Program (MS-STEP): alignment of current practices to the 2020 Canadian MS Working Group recommendations
Bibliographic record
Abstract
Background: New Canadian treatment optimization recommendations (TOR) were released in 2020 to guide clinicians on the optimal use of disease modifying treatments (DMTs). The alignment of current practices to TOR was investigated to identify potential areas for improvement in patient care. Methods: From January–July 2021, a chart audit of 160 patients was conducted by a sample of Canadian neurologists. Patient selection criteria included adult patients with relapsing-remitting MS, who had been switched from an initial DMT. Results: In alignment with TOR, most patients received a platform therapy initially (89%; n=143) and suboptimal efficacy response (MRI changes, relapses, disability progression) was the most common trigger for switching treatment. Furthermore, the expanded disability status scale was used in 94% (n=151) of cases during clinical assessment. In some instances, neurologists did not adhere to TOR. Only 10% (n=16) of patients were tested for cognitive function and over half (58%; n=93) did not receive gadolinium contrast at re-baseline MRI. Major criteria for switching therapies based on relapse rate, severity/recovery, or MRI were not followed in (n=4; n=27; n=7) patients respectively. Conclusions: Canadian neurologists are generally aligned with recent TOR for MS. However, they are not switching nearly as often or as early as per TOR criteria.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".