Automated Cuff Pressure Modulation
Bibliographic record
Abstract
OBJECTIVE: To assess whether dynamically modulating endotracheal tube (ETT) cuff pressure, by decreasing it during each ventilatory cycle instead of maintaining a constant level, would reduce the extent of intubation-related laryngotracheal injury. DESIGN: Single-blind, randomized controlled animal study using a previously validated live porcine model of accelerated intubation-related tracheal injury. SETTING: Animal research facility. PATIENTS: Ten piglets (weight, 16-20 kg each) were anesthetized and underwent intubation using a cuffed ETT. INTERVENTIONS: The animals were randomized into the following 2 groups: 5 pigs had a novel device to modulate their cuff pressure from 25 cm H₂O during inspiration to 7 cm H₂O during expiration, and 5 pigs had a constant cuff pressure of 25 cm H₂O. Both groups underwent ventilation under hypoxic conditions for 4 hours. MAIN OUTCOME MEASURE: Laryngotracheal mucosal injury after blinded histopathological assessment. RESULTS: The modulated-pressure group showed significantly less overall laryngotracheal damage than the constant-pressure group (mean grades, 1.2 vs 2.1; P < .001). Subglottic damage and tracheal damage were significantly less severe in the modulated-pressure group (mean grades, 1.0 vs 2.2; P < .001, and 1.9 vs 3.2; P < .001, respectively). There was no significant difference in glottic or supraglottic damage between the groups (P = .06 and .27, respectively). CONCLUSIONS: This novel device reduces the risk of subglottic and tracheal injury by modulating ETT cuff pressure in synchronization with the ventilatory cycle. This finding could have far-reaching implications for reducing the risk of airway injury in patients undergoing long-term intubation. Further clinical study of this device is warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".