Impact of infections on the incidence of acute inflammatory demyelinating polyneuropathy in children
Bibliographic record
Abstract
Abstract Objectives Acute inflammatory demyelinating polyneuropathy (AIDP) is the leading cause of acute flaccid paralysis in children and hypothesized to be triggered by antecedent infection. We sought to determine the association between AIDP and commonly acquired community infections in children. We utilized the reduction in these infections due to measures during coronavirus disease 2019 (COVID‐19) to serve as a natural experiment and determine their contribution to AIDP. Methods This cross‐sectional study used administrative and billing data from children's hospitals contributing to the Pediatric Health Information System. We included hospitalizations of children with a diagnosis of AIDP from (January 2017 through February 2021). Encounters for infection‐ (including respiratory, gastrointestinal, and COVID‐19) related diagnoses were measured as a marker of community incidence. Results A total of 1111 index encounters for AIDP were included. Pre‐COVID‐19, AIDP was not associated with respiratory or gastrointestinal infections, specifically, influenza or campylobacter. During the COVID‐19 period from March 2020 to February 2021, respiratory, gastrointestinal, and influenza infections decreased compared to expected (for the same time of year pre‐COVID‐19) by 59.6%–90.1%, 51.5%–68.9%, and 54.5%–97.9%, respectively. In contrast, AIDP hospitalizations and all hospitalizations only decreased by 11.5%–39.3% and 14.2%–25%, respectively. COVID‐19 was not positively associated with AIDP overall or at individual hospitals. Interpretation Common community‐acquired infections including COVID‐19 were not strongly associated with hospitalizations for AIDP in children. AIDP persisted despite the dramatic reduction in infection‐related encounters during the pandemic. These results suggest that recent antecedent community‐acquired infections were not the primary driver of AIDP and that alternative triggers should be explored.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".