Esophageal Manometry and Regional Transpulmonary Pressure in Lung Injury
Bibliographic record
Abstract
RATIONALE: Esophageal manometry is the clinically available method to estimate pleural pressure, thus enabling calculation of transpulmonary pressure (Pl). However, many concerns make it uncertain in which lung region esophageal manometry reflects local Pl. OBJECTIVES: To determine the accuracy of esophageal pressure (Pes) and in which regions esophageal manometry reflects pleural pressure (Ppl) and Pl; to assess whether lung stress in nondependent regions can be estimated at end-inspiration from Pl. METHODS: In lung-injured pigs (n = 6) and human cadavers (n = 3), Pes was measured across a range of positive end-expiratory pressure, together with directly measured Ppl in nondependent and dependent pleural regions. All measurements were obtained with minimal nonstressed volumes in the pleural sensors and esophageal balloons. Expiratory and inspiratory Pl was calculated by subtracting local Ppl or Pes from airway pressure; inspiratory Pl was also estimated by subtracting Ppl (calculated from chest wall and respiratory system elastance) from the airway plateau pressure. MEASUREMENTS AND MAIN RESULTS: In pigs and human cadavers, expiratory and inspiratory Pl using Pes closely reflected values in dependent to middle lung (adjacent to the esophagus). Inspiratory Pl estimated from elastance ratio reflected the directly measured nondependent values. CONCLUSIONS: These data support the use of esophageal manometry in acute respiratory distress syndrome. Assuming correct calibration, expiratory Pl derived from Pes reflects Pl in dependent to middle lung, where atelectasis usually predominates; inspiratory Pl estimated from elastance ratio may indicate the highest level of lung stress in nondependent "baby" lung, where it is vulnerable to ventilator-induced lung injury.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".