Survey of Cuff Management Practices in Intensive Care Units in Australia and New Zealand
Bibliographic record
Abstract
BACKGROUND: Cuff management varies widely in Europe and North America. Little is known about current practice in Australia and New Zealand. OBJECTIVE: To characterize important aspects of cuff management in intensive care units in Australia and New Zealand to compare with international reports. METHODS: A questionnaire was sent to all nurse managers of adult intensive care units in Australia and New Zealand. RESULTS: Survey response was 53% (92/175). After intubation, most units (50/92, 54%) used both minimal occlusive volume technique and cuff pressure measurement; 5 (5.5%) used these methods along with pilot balloon palpation. Twenty units (22%) used cuff pressure measurement exclusively and 16 units (17.5%) used the minimal occlusive volume technique exclusively. Only 1 unit (1%) used the minimal leak technique after intubation. For ongoing management, cuff pressure measurement was the preferred method, used exclusively in 42 units (46%), with the minimal occlusive volume technique used in 40 units (43%; sole method in 6 units [7%]) and palpation in 4 units (4%). In most units (65/92, 71%), cuffs were monitored once per nursing shift. In units using the minimal occlusive volume technique, oropharyngeal suctioning (74%) and semirecumbent positioning (58%) were routinely incorporated; sigh breaths (6%), discontinuation of enteral feeding (10%), and nasogastric tube aspiration (26%) were uncommon. Cuff management protocols (37%) and subglottic suctioning (12%) were used infrequently. CONCLUSIONS: Cuff pressure measurement was the preferred method, used exclusively or in combination with other methods. The minimal occlusive volume technique was used more often after intubation than for ongoing management.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".