Evaluation of Bronchiolitis in the Pediatric Population in the United States of America and Canada: A Ten-Year Review
Bibliographic record
Abstract
Bronchiolitis is a well-known viral infection among the pediatric population, significantly impacting hospitalization rates. The COVID-19 pandemic profoundly affected respiratory viral infections, including bronchiolitis, as various mitigation measures were implemented. In this study, we analyzed bronchiolitis cases during the pandemic and post-pandemic period, aiming to identify changes in management guidelines and their incidence and management over the last 10 years. Moreover, we explored the relationship between bronchiolitis and COVID-19, a virus that gained rapid notoriety worldwide. By analyzing data from pediatric populations in Canada and the USA, we sought to understand the role of varying seasons in the peak periods of bronchiolitis infections. The comprehensive review's results will provide valuable insights into bronchiolitis dynamics within the context of the COVID-19 pandemic. Our aim is to better comprehend the interplay between bronchiolitis, COVID-19, and seasonal variations, ultimately contributing to a deeper understanding of this respiratory viral infection and informing future management strategies. Furthermore, these findings can assist healthcare professionals in preparing for and responding to potential fluctuations in bronchiolitis cases in the post-pandemic era.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".