Respiratory hospitalizations and ICU admissions among children with and without medical complexity at the end of the COVID‐19 pandemic
Bibliographic record
Abstract
Decreased severe respiratory illness was observed during the first 2 years of the COVID-19 pandemic, with a relatively smaller decrease among children with medical complexity (CMC) compared to non-CMC. We extended this analysis to the third pandemic year (April 1, 2022, to March 31, 2023) when pandemic public health measures were loosened. A population-based repeated cross-sectional study evaluated respiratory hospitalizations among CMC and non-CMC (<18 years) in Ontario, Canada. Among the 67,517 CMC and 3,006,504 non-CMC in Ontario, there were more CMC respiratory hospitalizations compared with the expected prepandemic levels (n = 3145 hospitalizations, corresponding to rate ratio [RR], 1.20; 95% confidence interval [CI], 1.16-1.25) with an even larger relative increase among non-CMC (n = 6653, RR, 1.36; 95% CI, 1.34-1.38). Increased intensive care unit admissions for respiratory illness were also observed (CMC: RR, 1.44; 95% CI, 1.31-1.59; non-CMC: RR, 2.02; 95% CI, 1.89-2.16). Understanding respiratory surge drivers may provide insights to protect at-risk children from respiratory morbidity.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".