Decreased Incidence of Pediatric Neuro-Autoimmune Disorders During COVID-19 Pandemic Restrictions
Bibliographic record
Abstract
Infections are hypothesized to trigger certain autoimmune diseases; however, there is a lack of epidemiologic data surrounding pediatric neuro-autoimmune disorders during the COVID-19 pandemic. Our retrospective study assessed the incidence of pre-defined autoimmune disorders diagnosed at the Children's Hospital of Eastern Ontario in Ottawa, Canada, between October 2017 and June 2024. Inpatient and outpatient charts were queried to identify subjects with neuro-autoimmune disorders or type 1 diabetes as a nonneurologic autoimmune comparison group. Monthly incidences were calculated and compared between 3 COVID-19 pandemic restriction periods: the prerestrictions period (October 2017–March 2020), intrarestrictions period (April 2020–June 2022), and postrestrictions period (July 2022–June 2024). Poisson regression models were fit to the incidence data. New diagnoses of neuro-autoimmune disorders and type 1 diabetes were identified in 111 and 670 subjects, respectively. Incidence of neuro-autoimmune disorders, but not type 1 diabetes, decreased during the intrarestrictions period when compared to the prerestrictions period (incidence rate ratio = 0.57, 95% confidence interval 0.33-0.95, P < .05).
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".