Changes in immune indicators and bacteriologic profile were associated with patients with ventilator-associated pneumonia
Bibliographic record
Abstract
The aim of this study is to explore and identify ventilator-associated pneumonia (VAP)-related prognostic immune factors and further detect the drug-resistant pathogens to establish the theoretical guidance for clinical prevention and treatment strategies of VAP. A total of 478 patients using ventilator who were hospitalized in July 2014 to November 2016 in our hospital were enrolled in this study. About 103 patients with VAP (21.5%, 103/478) among 478 cases of patients using ventilator. Among the 103 patients with VAP, the distribution of pathogenic bacteria and drug resistance in patients with VAP were detected and analyzed. In the VAP group, 35 patients died and 43 patients had simultaneous sepsis. Compared with those of non-VAP group, the proportion of CD3 (P = .012), CD3CD4 (P = .024) and CD8CD28 ( P = .017) T cells in VAP group increased significantly, which indicated more severe immune response. Multivariate regression model analysis revealed that tracheotomy of mechanical ventilation (P = .013), mechanical ventilation time ≥7 days (P = .02) and aspiration and reflux (P = .011) were independent risk factors associated with VAP. According to the results of bacterial culture and drug sensitivity test, rational selection of antibiotics and monitoring of patients within intensive care unit can effectively control the incidence of VAP and improve the prognosis of patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".