Populations at Risk for Severe or Complicated Avian Influenza H5N1: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Little is known about risk factors for severe outcomes in patients infected with H5N1 and no systematic review has been conducted. Understanding risk factors is an important step for prioritizing prophylaxis or treatment in the event of a pandemic. OBJECTIVES: To systematically evaluate risk factors for severe outcomes in patients with avian influenza H5N1 infection. DATA SOURCES: MEDLINE, EMBASE, CINAHL, GlobalHealth, and CENTRAL through March 2011. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Observational studies of any design published in English, French, Spanish, German or Korean that reported on risk factor-outcome combinations of interest in participants with confirmed H5N1 infections. Outcomes considered included death, ventilator support, hospital and ICU admission, pneumonia, and composite outcomes. STUDY APPRAISAL: Risk of bias was assessed using the Newcastle-Ottawa scale (NOS). RESULTS: We identified 20 studies reporting on 999 patients infected with H5N1. The majority of studies (n = 14, 70%) were at intermediate risk of bias, i.e. 4-6 points on the NOS. Females were at increased risk of death (OR 1.75, 95% CI 1.27-2.44), while young age, in particular <5 years of age (OR 0.44, 95% CI 0.25-0.79 for death), was protective. Data on traditional risk factors was scarce and requires further studies. Another major limitation in the published literature was lack of adjustment for confounders. INTERPRETATION: Females were at increased risk for complications following H5N1 infection while young age protected against severe outcomes. Research on traditional risk factors was limited and is required.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.036 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".