Swiftsure complete care system in intubated adults: a feasibility study
Bibliographic record
Abstract
BACKGROUND: Mechanically ventilated patients are at risk of respiratory complications, which may be reduced by proper oral care. The oral cleaning device (Swishkit, Swiftsure, Vancouver, Canada) was developed to facilitate cleansing, moisturization, and debris removal in intubated patients. We aimed to evaluate the feasibility and usability of this device in adult intensive care patients. METHODS: We conducted a prospective, single-center study at the Cleveland Clinic. Adult patients recovering from cardiac surgery and expected to remain intubated ≥2 hours were included. Feasibility was defined as successful completion of the procedure by nursing staff in addition to standard care. Usability was assessed with the Device Use Questionnaire (DUQ), scored on a 5-point scale (1 = easiest, 2.5 = neutral, 5 = difficult). RESULTS: Twenty-two patients were enrolled. The procedure was completed in 21/22 (95.5%). One attempt failed due to jaw clenching that prevented device placement, but this patient was included in the analysis. DUQ scores ranged from 1.3 to 2.3, reflecting easy to neutral usability. More than 85% of responses to usability questions were positive, and no residual saline was observed in the subglottic region. CONCLUSION: Oral cavity cleansing with the oral cleaning device proved feasible and clinically usable in intubated patients recovering from cardiac surgery. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05578599.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".