Bibliographic record
Abstract
PURPOSE OF REVIEW: Esophageal pressure measurement well estimates pleural pressure. The interpretation of absolute values is often debated for various reasons, but the changes in pressure measured are considered very accurate provided that a number of precautions are taken. The information provided by these measurements is unique in nature and has an enormous potential to influence management. It allows to study the exact influence of the chest wall and to determine the real lung distending pressure. It is also the only way to quantify respiratory muscle activity and the work of breathing. RECENT FINDINGS: The application of esophageal pressure monitoring potentially covers a large field, especially for what concerns mechanical ventilation. This goes from the acute phase of the acute respiratory distress syndrome (ARDS) to weaning and patient-ventilator interactions. During ARDS, recent findings indicate that this measurement may help titrating the level of positive end-expiratory pressure or to determine the well tolerated upper limit of airway pressure. SUMMARY: Application of esophageal pressure monitoring is limited by technical issues, the need for background physiological knowledge and the fact that very few studies have assessed a direct influence of this measurement on patients' outcome. The technique is underused in everyday practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".