Does esophageal pressure monitoring reliably permit to estimate transpulmonary pressure in children?
Bibliographic record
Abstract
Objectives: This study aims to determine whether Transpulmonary Pressure can be measured in children using Esophageal Pressure as a surrogate of Pleural Pressure (P PL ).To reach this goal, we wanted to validate the reliability of two Esophageal Pressure recording methods compared to direct P PL measurement in situ. Study design: This is a prospective study.Methodology: Mechanically ventilated children were included if they had at least one chest tube.P PL was directly measured into the existing chest tube (P CH-TUBE ).Esophageal Pressure was measured by two methods: a catheter mounted pressure transducer system (P ES-REF ) and the preexisting nasogastric feeding tube pulled out in order to be located in the mid third of the esophagus (P ES-FT ).Results: Twelve patients were enrolled, and eight patients (median age: 4 months) were included in the analysis.For each method, the 2 measurements obtained with the same method were concordant.In the Bland-Altman analysis, the limits of agreement were wide for all between-method comparisons, from ± 8 to ±15 cm H 2 O.Conclusion: Prior to consider its use in clinical practice, in particular for the titration of the ventilatory support, it is essential to conduct more research in order to validate the measurement technique of Esophageal Pressure and confirm that it can accurately reflect the P PL .
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".