COVID-19 in people with neurofibromatosis 1, neurofibromatosis 2, or schwannomatosis
Bibliographic record
Abstract
ABSTRACT Purpose People with pre-existing conditions may be more susceptible to severe Coronavirus disease 2019 (COVID-19) when infected by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The relative risk and severity of SARS-CoV-2 infection in people with rare diseases like neurofibromatosis (NF) type 1 (NF1), neurofibromatosis type 2 (NF2), or schwannomatosis (SWN) is unknown. Methods We investigated the proportions of SARS-CoV-2 positive or COVID-19 patients in people with NF1, NF2, or SWN in the National COVID Collaborative Cohort (N3C) electronic health record dataset. Results The cohort sizes in N3C were 2,501 (NF1), 665 (NF2), and 762 (SWN). We compared these to N3C cohorts of other rare disease patients (98 - 9844 individuals) and the general non-NF population of 5.6 million. The site- and age-adjusted proportion of people with NF1, NF2, or SWN who tested positive for SARS-CoV-2 or were COVID-19 patients (collectively termed positive cases ) was not significantly higher than in individuals without NF or other selected rare diseases. There were no severe outcomes reported in the NF2 or SWN cohorts. The proportion of patients experiencing severe outcomes was no greater for people with NF1 than in cohorts with other rare diseases or the general population. Conclusion Having NF1, NF2, or SWN does not appear to increase the risk of being SARS-CoV-2 positive or of being a COVID-19 patient, or of developing severe complications from SARS-CoV-2.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".