Allergic diseases, COVID-19 pandemic, and underlying mechanisms
Bibliographic record
Abstract
The impact of COVID-19 on allergic diseases in adolescents is not well described. Although there has been a previous study that examined changes in prevalence one year into the pandemic, there is a need for follow-up due to the nature of the infectious disease. Therefore, this article explored the extent to which the pandemic had a potential mediating effect on the prevalence of allergic diseases in adolescents. Previous study suggested the weighted prevalence of asthma decreased from 2009 to 2021 both before and during the pandemic. The weighted prevalence of allergic rhinitis increased from 2009 to 2019; however, there was a decrease in 2020 followed by a minute increase in 2021. Similarly, the weighted prevalence of atopic dermatitis increased from 2009 to 2019, and decreased from 2020 to 2021. These results suggest that the prevalence of allergic diseases decreased during the pandemic, with some exceptions. In light of the findings, we would like to encourage continued efforts to monitor allergic diseases in the years to come, even after the pandemic is over. It is also recommended that researchers from other countries conduct their own research on the respective topic that can develop a general consensus and confirm the reliability and validity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".