Obesity as a risk factor for severe influenza infection in children and adolescents: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Purpose Obesity has been recently identified as a predisposing factor for worse prognosis in viral illnesses such as SARS-CoV-2, however, its role in children with influenza is not yet clarified. The current systematic review and meta-analysis aims to assess whether obesity is a risk factor for either hospitalization or worse prognosis when hospitalized among children infected by influenza. Methods We systematically searched the following databases: MEDLINE, Clinicaltrials.gov, and Cochrane Central Register of Controlled Trials (CENTRAL) using a structured algorithm. Statistical meta-analysis was performed using the ‘meta’ package in R software and included studies were evaluated using the Newcastle-Ottawa scale. ResultsAmong children with influenza, obesity increased the odds of hospitalization with a pooled OR of 1.89 (95% CI [1.23, 2.9], I2=0, p=0.003). When hospitalized, children with obesity were also more likely to have a worse outcome than their healthy-weight counterparts, with a pooled OR of 1.24 (95% Cl [1.02;1.51], I2=11, p=0.03). In an effort to lower heterogeneity Leave-one-out meta-analysis was conducted. Publication bias was assessed with the visual inspection of funnel plots and the trim-and-fill method. Certainty assessment was evaluated using the GRADE score. ConclusionsThe findings of our meta-analysis suggest that obesity in children with influenza is associated with a worse prognosis; both hospitalization and ICU admission/death. //
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.043 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".