Effects of Pleural Effusion Drainage on Oxygenation, Respiratory Mechanics, and Hemodynamics in Mechanically Ventilated Patients
Bibliographic record
Abstract
OBJECTIVES: In mechanically ventilated patients, the effect of draining pleural effusion on oxygenation is controversial. We investigated the effect of large pleural effusion drainage on oxygenation, respiratory function (including lung volumes), and hemodynamics in mechanically ventilated patients after ultrasound-guided drainage. Arterial blood gases, respiratory mechanics (airway, pleural and transpulmonary pressures, end-expiratory lung volume, respiratory system compliance and resistance), and hemodynamics (blood pressure, heart rate, and cardiac output) were recorded before and at 3 and 24 hours (H24) after pleural drainage. The respiratory settings were kept identical during the study period. MEASUREMENTS AND MAIN RESULTS: The mean volume of effusion drained was 1,579 ± 684 ml at H24. Uncomplicated pneumothorax occurred in two patients. Respiratory mechanics significantly improved after drainage, with a decrease in plateau pressure and a large increase in end-expiratory transpulmonary pressure. Respiratory system compliance, end-expiratory lung volume, and PaO2/FiO2 ratio all improved. Hemodynamics were not influenced by drainage. Improvement in the PaO2/FiO2 ratio from baseline to H24 was positively correlated with the increase in end-expiratory lung volume during the same time frame (r = 0.52, P = 0.033), but not with drained volume. A high value of pleural pressure or a highly negative transpulmonary pressure at baseline predicted limited lung expansion following effusion drainage. A lesser improvement in oxygenation occurred in patients with ARDS. CONCLUSIONS: Drainage of large (≥500 ml) pleural effusion in mechanically ventilated patients improves oxygenation and end-expiratory lung volume. Oxygenation improvement correlated with an increase in lung volume and a decrease in transpulmonary pressure, but was less so in patients with ARDS.
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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) | 0.001 | 0.001 |
| 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".