Bench-to-bedside review: Recruitment and recruiting maneuvers.
Bibliographic record
Abstract
In patients with acute respiratory distress syndrome (ARDS), the lung comprises areas of aeration and areas of alveolar collapse, the latter producing intrapulmonary shunt and hypoxemia. The currently suggested strategy of ventilation with low lung volumes can aggravate lung collapse and potentially produce lung injury through shear stress at the interface between aerated and collapsed lung, and as a result of repetitive opening and closing of alveoli. An 'open lung strategy' focused on alveolar patency has therefore been recommended. While positive end-expiratory pressure prevents alveolar collapse, recruitment maneuvers can be used to achieve alveolar recruitment. Various recruitment maneuvers exist, including sustained inflation to high pressures, intermittent sighs, and stepwise increases in positive end-expiratory pressure or peak inspiratory pressure. In animal studies, recruitment maneuvers clearly reverse the derecruitment associated with low tidal volume ventilation, improve gas exchange, and reduce lung injury. Data regarding the use of recruitment maneuvers in patients with ARDS show mixed results, with increased efficacy in those with short duration of ARDS, good compliance of the chest wall, and in extrapulmonary ARDS. In this review we discuss the pathophysiologic basis for the use of recruitment maneuvers and recent evidence, as well as the practical application of the technique.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".