Perceptions of risk and adherence to care in ms patients during the COVID-19 pandemic: a cross-sectional study
Bibliographic record
Abstract
Background: The Sars-CoV-2 (Covid19) pandemic has caused >100,000 deaths in the USA and has the potential to disrupt the care of NMO patients to a great extent A unified and thorough set of guidelines for NMO management across countries has yet to be established Objectives: To document the prescribing and treatment patterns of North American neurologists with expertise in NMO patient care during the Covid19 pandemic Methods: We created a new survey instrument to query the practices, decision-making, and perspectives of a group of neuroimmunology- focused neurologists who actively care for patients with NMO in the USA or Canada Survey responses included rating of statements for agreement, open-ended questions, multiple choice, and estimates of current practices in gradient forms The survey was circulated from April 14, 2020 to May 4, 2020 Results: 192 neurologists met our inclusion criteria and were most often from academic hospitals (51%), followed by single specialty groups (21%) The volume of in-person NMO visits declined from 4 NMO patients per typical month pre-Covid19 to <1 patient in the prior month (approximately April 2020) More than half of neurologists indicated NMO patients were delaying their scheduled MRIs (57%), two-thirds indicated patients were delaying clinical visits (67%) and roughly half indicated patients were delaying laboratory testing (52%) due to fear of contracting Covid19 Seventeen percent of neurologists have deferred one or more doses of NMO patients' immunosuppressive drug, 16% changed the dosing interval, and 15% switched health facilitybased infusions to home-based infusions Roughly half of all neurologists were uncomfortable with prescribing tocilizumab (48%), though 19% of neurologists anticipated using tocilizumab more often for their NMO patients given its current investigation as a treatment for Covid19 Conclusions: The prescribing patterns and treatment decisions of NMO care providers during the Covid19 pandemic indicate a need for evidence-based, comprehensive guidelines for treating NMO patients amid healthcare crises moving forward
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".