3018 A multi-centre longitudinal study analysing disease modifying therapy prescribing patterns during the COVID-19 pandemic
Bibliographic record
Abstract
Background/ Objectives The COVID-19 pandemic raised concern amongst clinicians that disease-modifying therapy (DMT), particularly anti-CD20 monoclonal antibodies (mAB) and fingolimod, could worsen COVID-19 in people with multiple sclerosis (pwMS). This study aimed to examine DMT prescribing trends pre- and post-pandemic.Methods A multi-centre longitudinal study with 8,771 participants was conducted using data from the MSBase COVID-19 sub-study. Trends in DMT prescribing between 2018–2022 were analysed using multivariable mixed-effects logistic regression. DMT-initiation referred to the first prescription of any DMT in that timeframe, DMT-switches denoted a change in DMT regimen within 6 months of last DMT use.Results Post-pandemic, there was a significant increase in DMT initiation/switching to natalizumab and cladribine ([Natalizumab-Initiation:OR 1.72, 95% CI 1.39–2.13;Switching:OR 1.66, 95% CI 1.40–1.98],[Cladribine-Initiation:OR 1.43, 95% CI 1.09–1.87;Switching:OR 1.67, 95% CI 1.41–1.98]). Anti-CD20 mABs initiation decreased during-pandemic but recovered post-pandemic. Overall, anti-CD20 mABs initiation/switching increased, however less than other high-efficacy DMTs(Initiation:OR 1.26, 95% CI 1.06–1.49;Switching:OR 1.15, 95% CI 1.02–1.29). Initiation/switching of fingolimod, interferon-beta, and alemtuzumab significantly decreased([Fingolimod-Initiation:OR 0.55, 95% CI 0.41–0.73;Switching:OR 0.49, 95% CI 0.41–0.58],[Interferon-Initiation:OR 0.48, 95% CI 0.41–0.57; Switching:OR 0.78, 95% CI 0.62–0.99],[Alemtuzumab-Initiation:OR 0.27, 95% CI 0.15–0.48;Switching:OR 0.27, 95% CI 0.17–0.44]). Dimethyl fumarate initiation increased, while switching decreased(Initiation: OR 1.76, 95% CI 1.49–2.09;Switching:OR 0.85, 95% CI 0.69–1.05).Conclusion Post-pandemic, clinicians preferentially prescribed natalizumab and cladribine over anti-CD20 mABs and fingolimod, likely to preserve efficacy but reduce perceived risk of immunosuppression. This has clinical implications for disease progression and highlights the importance of equitable access to DMTs and COVID-19 treatment in a pandemic to ensure continued use of high-efficacy 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".