Symptoms in Patients Receiving Noninvasive Ventilation in the Intensive Care Unit
Bibliographic record
Abstract
BACKGROUND: Although a multitude of studies have demonstrated the effectiveness of noninvasive ventilation (NIV) for treatment of respiratory insufficiency, there have been few investigations of patients' experiences while receiving this common treatment. Identification of the presence, intensity, and distress of symptoms during NIV will inform the development and testing of interventions to best manage them and improve patients' intensive care unit (ICU) experiences. OBJECTIVE: The objectives of this study were (a) to identify the presence, intensity, and distress of symptoms in patients receiving NIV in the ICU using a modified version of the Edmonton Symptom Assessment Scale (MESAS) and (b) to describe the most common and distressing symptoms experienced by patients. METHODS: A cross-sectional descriptive design was used with a convenience sample of 114 participants enrolled from three ICUs at one Midwestern medical center. Participants were approached if they were English-speaking, were 18 years old or older, and had an active order for NIV; exclusions included use of personal NIV equipment, severe cognitive impairment, or problems communicating. Demographic and clinical data were obtained from the electronic health record. Presence, intensity, and distress of patient-reported symptoms were obtained once using a modified, 11-item version of the MESAS. RESULTS: The mean age of participants was 68 years old, and 54.4% were male. The primary type of NIV was bi-level positive airway pressure; a nasal/oral mask was most frequently used. The symptoms experienced by most of the participants were thirst, anxiety, tiredness, and restlessness; these symptoms were rated as moderate or severe in both intensity and distress by most participants experiencing the symptoms. DISCUSSION: Patients in the ICU experience both intense and distressful symptoms that can be severe while undergoing treatment with NIV. Future research is warranted to determine these symptoms' interrelatedness and develop interventions to effectively manage patient-reported symptoms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".