Utility and safety of draining pleural effusions in mechanically ventilated patients: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Pleural effusions are frequently drained in mechanically ventilated patients but the benefits and risks of this procedure are not well established. METHODS: We performed a literature search of multiple databases (MEDLINE, EMBASE, HEALTHSTAR, CINAHL) up to April 2010 to identify studies reporting clinical or physiological outcomes of mechanically ventilated critically ill patients who underwent drainage of pleural effusions. Studies were adjudicated for inclusion independently and in duplicate. Data on duration of ventilation and other clinical outcomes, oxygenation and lung mechanics, and adverse events were abstracted in duplicate independently. RESULTS: Nineteen observational studies (N = 1,124) met selection criteria. The mean PaO2:FiO2 ratio improved by 18% (95% confidence interval (CI) 5% to 33%, I2 = 53.7%, five studies including 118 patients) after effusion drainage. Reported complication rates were low for pneumothorax (20 events in 14 studies including 965 patients; pooled mean 3.4%, 95% CI 1.7 to 6.5%, I2 = 52.5%) and hemothorax (4 events in 10 studies including 721 patients; pooled mean 1.6%, 95% CI 0.8 to 3.3%, I2 = 0%). The use of ultrasound guidance (either real-time or for site marking) was not associated with a statistically significant reduction in the risk of pneumothorax (OR = 0.32; 95% CI 0.08 to 1.19). Studies did not report duration of ventilation, length of stay in the intensive care unit or hospital, or mortality. CONCLUSIONS: Drainage of pleural effusions in mechanically ventilated patients appears to improve oxygenation and is safe. We found no data to either support or refute claims of beneficial effects on clinically important outcomes such as duration of ventilation or length of stay.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.002 | 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".