P.014 Decision-making in the use of corticosteroids for treating multiple sclerosis relapses: a retrospective study from a single Canadian center
Bibliographic record
Abstract
Background: Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system characterized by acute attacks. High-dose steroids (HDS) are the primary treatment, with no significant differences between oral and intravenous (IV) routes. However, factors influencing route selection and attack characteristics leading to treatment remain unclear. This study assesses trends in oral vs. IV HDS use, factors affecting decisions, and clinical impact. Methods: We retrospectively analyzed data from the Multiple Sclerosis database (MuSicaL) using Natural Language Processing (NLP) from 2010–2022. We examined annual trends in HDS route, its relationship with attack type, and prescribing specialties. Statistical analyses were conducted using R-4.2.2. Results: Of 2,413 individuals meeting inclusion criteria, 1,086 had an attack, and 543 (50%) used HDS. Among 265 with a known route, oral HDS was most common, and HDS use declined after 2018. Attack type significantly influenced HDS route (p = 0.045), with IV use highest in multifocal subtype (50.9%) and lowest in myelitis (32.7%). Neurologists were the primary prescribers of IV HDS. Conclusions: Our results indicate a trend towards increased oral HDS use, with IV reserved for severe attacks like multifocal ones. Attack type influences treatment choices, and neurologists remain key prescribers of IV HDS, guiding future treatment strategies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".