Utility of draining pleural effusions in mechanically ventilated patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pleural effusions are prevalent in mechanically ventilated patients, and clinicians frequently consider draining the effusions. It is controversial whether patients benefit from pleural drainage in terms of either physiological or clinical outcomes. RECENT FINDINGS: Pleural drainage may be undertaken for a variety of reasons. Effusions are an important potential source of infection in patients with undifferentiated sepsis. Pleural drainage may improve hypoxemia or lung mechanics, but the physiological response depends on a complex interplay between lung and chest wall compliance, applied positive end-expiratory pressure and drainage volume. Pleural effusions may be associated with significant cyclic lung recruitment and collapse during tidal ventilation. Because effusions are primarily accommodated by descent of the diaphragm, they can also impair diaphragm mechanics significantly. There is very limited data in the literature to support the use of pleural drainage to accelerate liberation from mechanical ventilation, and there are no randomized controlled trials published to date. SUMMARY: Pleural drainage may benefit certain patient populations based on individual physiological considerations, but randomized controlled trials evaluating the impact on weaning outcomes are lacking. Future research efforts should focus on identifying patient populations most likely to benefit and clarify the mechanisms by which weaning may be accelerated after pleural drainage.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".