High-risk primary disease and medical factors of ventilator-associated pneumonia in neonates: A Meta-analysis
Bibliographic record
Abstract
Objective To provide the evidences for the management strategies of ventilator-associated pneumonia (VAP) in neonates, we systematically reviewed all related studies and analyzed the high-risk primary disease and medical factors of VAP in neonates. Methods We retrieved all related studies in CNKI, Wan-fang, VIP, CBM, Pubmed and Embase and evaluated their quality by Newcastle-Ottawa Scale and analyzed all data by qualitative and Meta-analysis. Results There were 12 case-control studies with higher methodological quality and involving 1 994 neonates and with 708 VAP patients. Six studies involving 872 neonates were in-cluded, the odds ratio of respiratory distress syndrome(OR=2.81) and malnutrition(OR=5.18) had significant differences between VAP and non-VAP group. Seven studies involving 1 110 neonates were included and the odds ratio of patients with corticosteroids (OR=3.12), central inhibitors (OR=2.31), antacids (OR=4.35) and Gamma globulin with large doses (OR=2.35) had significant differences between VAP group and non VAP. Four studies involving 554 neonates were included and the odds ratio of patients with closed chest drainage (OR=1.81)and umbilical vein catheterization (OR=9.19) had significant differences between VAP group and non VAP. Six studies involving 1 139 neonates were included and the odds ratio of patients with parenteral nutrition (OR=1.82)and blood transfusions (OR=2.49) had significant differences between VAP group and non VAP. Conclusions Our study confirms that the respiratory distress syndrome and malnutrition corticosteroids, central inhibitors, antacids, Gamma globulin with large doses, closed chest drainage, umbilical vein catheteriza-tion, parenteral nutrition and blood transfusions are important risk and early-warning factors. Key words: Neonates; Ventilator-associated pneumonia; Primary disease; Medical factors; Meta-analysis
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.055 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".