Manifestations and impact of the COVID‐19 pandemic in neuroinflammatory diseases
Bibliographic record
Abstract
Abstract Objective To report initial results of a planned multicenter year‐long prospective study examining the risk and impact of COVID‐19 among persons with neuroinflammatory disorders (NID), particularly multiple sclerosis (MS). Methods In April 2020, we deployed online questionnaires to individuals in their home environment to assess the prevalence and potential risk factors of suspected COVID‐19 in persons with NID (PwNID) and change in their neurological care. Results Our cohort included 1115 participants (630 NID, 98% MS; 485 reference) as of 30 April 2020. 202 (18%) participants, residing in areas with high COVID‐19 case prevalence, met the April 2020 CDC symptom criteria for suspected COVID‐19, but only 4% of all participants received testing given testing shortages. Among all participants, those with suspected COVID‐19 were younger, more racially diverse, and reported more depression and liver disease. PwNID had the same rate of suspected COVID‐19 as the reference group. Early changes in disease management included telemedicine visits in 21% and treatment changes in 9% of PwNID. After adjusting for potential confounders, increasing neurological disability was associated with a greater likelihood of suspected COVID‐19 (OR adj = 1.45, 1.17–1.84). Interpretations Our study of real‐time, patient‐reported experience during the COVID‐19 pandemic complements physician‐reported MS case registries which capture an excess of severe cases. Overall, PwNID seem to have a risk of suspected COVID‐19 similar to the reference population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".