Meta-analysis of high risk factors for ventilator-associated pneumonia in preterm newborns in China
Bibliographic record
Abstract
OBJECTIVE To systematically evaluate the relevant literatures about the high risk factors for ventilatorassociated pneumonia(VAP)in the preterm newborns in China and integratively analyze the characteristics of the high risk factors so as to provide guidance for the control of VAP in the preterm newborns.METHODS The relevant literatures were systematically retrieved from the databases of CNKI,Wanfang,VIP,CBM,Pubmed,and Embase,then the risk of bias of all included studies was evaluated by Newcastle-Ottawa scale(NOS),and the data were studied by using quantitative combination with qualitative analysis and meta-analysis.RESULTS Totally 11 case-control studies were conducted,involving 1 096children in 1999-2012,522(47.63%)cases of VAP;there were 21high risk factors for VAP in the preterm newborns.The meta-analysis showed that the difference in the mortality rate,incidence of anemia,re-intubation,or prophylactic use of antibiotics between the VAP group and the non-VAP group was significant(P0.01).The difference in the incidence of VAP between the group with gestational age less than 32weeks and the group with the gestational age no less than 32weeks,or between the group with the body weight less than 1500kg and the group with the body weight no less than 1500kg,or between the group with the time of use of ventilator less than 72hours and the group with the time of use of ventilator no less than 72hours was significant(P0.01).CONCLUSIONThe incidence of VAP is high in the preterm newborns,which is a leading cause of death;the related risk factors are complex and diversified;the study concludes that the body weight,gestational age,anemia,time of use of ventilator,re-intubation,and prophylactic use of antibiotics are the high risk factors.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".