Asymmetrical Lung Injury: Management and Outcome
Bibliographic record
Abstract
Among mechanically ventilated patients, asymmetrical lung injury is probably extremely frequent in the intensive care unit but the lack of standardized measurements does not allow to describe any prevalence among mechanically ventilated patients. Many past studies have focused only on unilateral injury and have mostly described the effect of lateral positioning. The good lung put downward might receive more perfusion while the sick lung placed upward receive more ventilation than supine. This usually results in better oxygenation but can also promote atelectasis in the healthy lung and no consensus has emerged on the clinical indication of this posture. Recently, electrical impedance tomography (EIT) has allowed for the first time to precisely describe the distribution of ventilation in each lung and to better study asymmetrical lung injury. At low positive-end-expiratory pressure (PEEP), a very heterogeneous ventilation exists between the two lungs and the initial increase in PEEP first helps to recruit the sick lung and protect the healthier lung. However, further increasing PEEP distends the less injured lung and must be avoided. The right level can be found using EIT and transpulmonary pressure. In addition, EIT can show that in the two lungs, airway closure is present but with very different airway opening pressures (AOPs) which cannot be identified on a global assessment. This may suggest a very different PEEP level than on a global assessment. Lastly, epidemiological studies suggest that in hypoxemic patients, the number of quadrants involved has a strong prognostic value. The number of quadrants is more important than the location of the unilateral or bilateral nature of the involvement for the prognosis, and hypoxemic patients with unilateral lung injury should probably be considered as requiring lung protective ventilation as classical acute respiratory distress syndrome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".