Impact of lockdowns on paediatric asthma hospital presentations over three waves of COVID-19 pandemic
Bibliographic record
Abstract
Public health measures to mitigate the COVID-19 pandemic have altered health care for chronic conditions. The impact of the COVID-19 pandemic on paediatric asthma, the most common chronic respiratory cause of childhood hospitalisation, in Australia, remains unknown. In a multicentre study, we examined the impact of three waves of COVID-19 on paediatric asthma in New South Wales Australia. Time series analysis was performed to determine trends in asthma hospital presentations in children aged 2-17 years before (2015-2019) and during the COVID-19 pandemic (2020-2021) using emergency department and hospital admission datasets from two large tertiary paediatric hospitals.In this first report from Australia, we observed a significant decrease in asthma hospital presentations during lockdown periods including April (68.85%), May (69.46%), December (49.00%) of 2020 and August (66.59%) of 2021 compared to pre-pandemic predictions.The decrease in asthma hospital presentations coincided with the lockdown periods during first, second and third waves of the COVID-19 pandemic and was potentially due to reduced transmission of other common respiratory viruses from restricted movement.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".