Socio-demographic risk factors of ventilator-associated pneumonia in neonates:A Meta-analysis
Bibliographic record
Abstract
Objective To provide the evidences for the early-warning strategies of ventilator-associated pneumonia (VAP) in neonates,we systematically reviewed all related studies and analyzed the socio-demographic risk factors of VAP in neonates.Methods We retrieved all related studies in CNKI,Wanfang,VIP,CBM,Pubmed and Embase and evaluated their risk of bias by Newcastle-Ottawa Scale and analyzed all data by Meta-analysis.Results There were 21 case-control studies involving 3 546 neonates and 1 201 VAP patients and 6 socio-demographic risk factors of VAP in neonates being included.The odds ratio of deaths had significant differences between VAP and non VAP group (OR=2.98).Five studies involving 1 031 neonates were included,the average hospitalization days was significantly different between VAP and non-VAP group (MD=9.33).The average gestational age was significandy different between VAP and non-VAP group (MD =-3.05) and the odds ratio of VAP was significantly different between ≤ 37 w group and ＞37 w group (OR=2.62),between 32～37 w group and ＞37 w group (OR=2.37) and between ＜32 w group and 32～37 w group (OR=2.72).Nineteen studies involving 3 238 neonates were included,the average weight was significantly different between VAP and non-VAP group (MD=-0.58) and the odds ratio of VAP was significantly different between＜2.5 kg and ≥2.5 kg group(OR=3.75),between 1.5～2.5 kg and ＞2.5 kg group (OR=2.42) and between ＜1.5 kg and 1.5～2.5 kg group (OR=3.02).Conclusions In neonates,VAP is an important cause of death which prolongs hospitalization and increases economic burden.Our study confirms that the gestational age (≤37 w) and weight(＜2.5 kg) increase the risk of VAP and all neonates with lower weight and smaller gestational age have higher risk of VAP.Therefore,the weight and gestational age are important risk and early-warning factors. Key words: Neonates; Ventilator-associated pneumonia; Socio-demographic risk 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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.046 |
| Bibliometrics | 0.007 | 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".