Assessment of aspiration risk from dynamic modulation of endotracheal tube cuff pressure
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To assess the risk of aspiration using a novel valve circuit that dynamically modulates endotracheal tube cuff pressure during the ventilatory cycle using bench and live animal models. STUDY DESIGN: Animal model. METHODS: The bench model consisted of a cuffed endotracheal tube inserted into an artificial trachea. Leakage of liquid around the cuff was measured after 4 hours of constant or dynamic modulation of cuff pressure at variable peak end expiratory pressures. In the porcine model, eight animals were ventilated with the modulating valve circuit and compared to eight controls ventilated with a constant cuff pressure (25 cm of water). Aspiration was monitored quantitatively using a pH probe (measured as voltage) and visually using fluoroscopy. RESULTS: There was no difference in the amount of fluid leakage around the endotracheal tube cuff in the constant or dynamically modulated pressure-cuff groups in the bench or animal models. CONCLUSION: Dynamically modulating endotracheal tube-cuff pressures to minimize tracheal mucosal damage does not increase the tendency to leak around the cuff compared to endotracheal tube cuffs inflated to a constant pressure. LEVEL OF EVIDENCE: N/A.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".