Researching COVID-19 in progressive MS requires a globally coordinated, multi-disciplinary and multi-stakeholder approach—perspectives from the International Progressive MS Alliance
Bibliographic record
Abstract
Background: The COVID-19 pandemic has reinforced the importance of research for the health of our society and highlighted the need for stakeholders of the health research and care continuum to form a collaborative and interdependent ecosystem. Objective: With the world still reeling from waves of the COVID-19 pandemic and adapting to the vaccine rollout at widely different rates, the International Progressive MS Alliance (hereafter Alliance) organized a meeting (April 2021) to consider how the Covid-19 pandemic impacts the health and well-being of people with progressive Multiple Sclerosis (MS). Methods: We invited the Alliance stakeholders and experts to present what they have learned about SARS-CoV-2 infection and progressive MS and to define future scientific priorities. Results: The meeting highlighted three priorities for additional focus: (1) the impact of Disease Modifying Therapies (DMTs) on the risk of COVID-19 and on the efficacy of COVID-19 vaccines in people with progressive MS; (2) the long-term impact of COVID-19 and COVID-19 vaccines on the biology of progressive MS; and (3) the impact on well-being of people with progressive MS. Conclusion: This paper's calls to action could represent a path toward a shared research agenda. Multi-stakeholder and long-term investigations will be required to drive and evolve such an agenda.
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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 | 0.083 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.010 | 0.013 |
| 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".