The prevalence of pediatric asthma hospitalizations at different stages of the COVID-19 pandemic: A systematic review and meta-analysis study protocol
Bibliographic record
Abstract
BACKGROUND: Asthma is a highly prevalent chronic inflammatory lung disease and is a frequent cause of hospitalization in children. The COVID-19 pandemic has introduced several challenges that have impacted the delivery of care for vulnerable patients, including asthmatic children. Asthmatic children without immediate access to healthcare services can face severe and fatal consequences. Furthermore, various governmental restrictions and viral mutants have been introduced throughout the pandemic, affecting COVID-19 cases and hospitalization rates. OBJECTIVES: To investigate the impact of the COVID-19 pandemic on the prevalence of asthma hospitalizations during various stages of the pandemic. We also aim to compare asthma hospital admissions during the pandemic to pre-pandemic periods. METHODS AND ANALYSIS: The databases PubMed (MEDLINE), EMBASE, CINAHL, and the Cochrane library will be used to identify relevant articles between the start of the pandemic and the date of the search strategy. Studies will be included if they examine hospital admissions for pediatric (0 to 18 years) asthma patients, regardless of asthma severity, sex, ethnicity or race. Observational retrospective cohort, prospective cohort, and cross-sectional studies will be included. A meta-analysis will be conducted if there are ≥2 articles. Else, a narrative review will be used to report our results. TRIAL REGISTRATION: PROSPERO registration number: CRD42022337606.
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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 | 0.028 | 0.041 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.022 | 0.027 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.003 |
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".