Investigating the Role of Subglottic Secretions Suctioning in the Prevention of Ventilator Associated Pneumonia in Patients With Invasive Mechanical Ventilation
Bibliographic record
Abstract
BACKGROUND: development of ventilator associated pneumonia (VAP) leads to prolonged hospital stay, increased health care cost, and mortality rates. Subglottic secretion drainage through a dedicated endotracheal tube has been advocated as a mean to decrease the incidence of VAP and thereby assisting in the decrease of morbidity associated with invasive mechanical ventilation. OBJECTIVE: Investigate the role of subglottic secretion suctioning in the prevention of VAP in mechanically ventilated patients in intensive care unit. METHODS: A cross sectional study done in the intensive care unit of Ghazi Al-Hariri hospital for surgical specialties in medical city complex, 30 patients who are in need for invasive mechanical ventilation were intubated with endotracheal tube that have special port for subglottic secretion suctioning. Daily monitoring of patients clinical and radiological data to detect features of VAP was done, and if there was a suspicion of pneumonia, culture for tracheal aspirate performed to confirm diagnosis. RESULTS: Patient’s age was 37.1 ± 15.39 years, the highest proportion of study patients was found in age group < 30 and 30–49 years (40% in each group), most of the patients were males (70%) with a male to female ratio of 2.33:1, Subglottic secretion suctioning lead to reduction in VAP by relative risk (95%CI) of 0.167 (0.045–0.559), p-value = 0.001. Twenty eight patients didn’t show any sign, symptoms or radiological features suggesting a diagnosis of pneumonia while two patients developed features of pneumonia (suggestive signs and symptoms, radiological features and positive culture of tracheal aspirate). CONCLUSION: the use of endotracheal tube with subglottic secretions suctioning can have a role in the prevention of VAP in mechanically ventilated 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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| 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".