Bibliographic record
Abstract
<div> Critically ill patients admitted to the intensive care unit (ICU) experience various symptoms and discomfort. Although thirst is a typical distressing symptom and should be assessed daily, it is crucial to understand its prevalence and risk factors in the ICU setting. Nevertheless, currently, systematic reviews of prevalence and risk factors are lacking. This study evaluated the prevalence and risk factors of thirst in critically ill patients. We conducted a comprehensive search of the MEDLINE, Cochrane Library, and CINAHL databases. The study design included cohort, cross-sectional, and intervention studies, including randomized and non-randomized controlled trials with control groups. The point estimates from each study were combined using a random-effects meta-analysis model. We aggregated the prevalence of thirst in ICU patients and calculated the point estimates and 95% confidence intervals. The risk of bias was assessed using the Cochrane Risk of Bias 2 tool and Newcastle-Ottawa Scale. Fifteen studies were eligible for inclusion, of which seven reported the prevalence of thirst. A total of 2,204 patients were combined, with a prevalence estimate of 0.70. The risk factors for thirst were categorized as patient and treatment factors: four patient factors (e.g., serum sodium concentration and severity of illness) and six treatment factors (e.g., nil per os and use of diuretics) were identified. However, the results showed high heterogeneity in the prevalence of thirst among critically ill patients. It was established that 70% of critically ill patients experienced thirst. Additional investigations are required to obtain a more comprehensive overview of thirst among these patients. Systematic review registration number The protocol was registered in PROSPERO (ID: CRD42023428619) on June 6, 2023. (URL: <a href="https://www.crd.york.ac.uk" target="_blank">https://www.crd.york.ac.uk</a>) </div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.115 | 0.006 |
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; both teacher heads agree on what is shown here.
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".