The assessment of delirium in patients with stroke in an intensive care unit – Integrative Literature Review
Bibliographic record
Abstract
Introduction: Cerebral Vascular Accident (CVA) causes changes at various levels in users, which can trigger delirium.However, it appears that identifying delirium in the initial phase of stroke is difficult in the presence of neurological deficits.For this reason, delirium is a common complication in an Intensive Care Unit (ICU), making regular monitoring of users' signs/symptoms crucial, with the need to use credible assessment instruments.Objectives: Analyze delirium assessment instruments; select the best scale to assess delirium in patients with stroke; identify obstacles that hinder the application of delirium assessment tools in patients with stroke.Methodology: This is an Integrative Literature Review, for which electronic databases such as Medline and CINHAL were used to carry out the research, using the PI[C] O method, and, finally, seven articles were selected scientific studies with a publication time frame between 2019 and 2021.Results: Ischemic stroke (IS) has a higher incidence.When applying instruments to assess the presence of delirium, the Confusion Assessment Method for Intensive Care Unit (CAM-ICU) is the instrument that presents the most limitations, as it requires interaction with users, unlike the Intensive Care Delirium Screening Checklist (ICDSC) which is observational.Still, other articles refer to the Confusion Assessment Method (CAM) and the Montreal Cognitive Assessment (MoCA) as more appropriate instruments.Conclusion: Delirium is often difficult to detect, as many cases can go unnoticed, especially in patients with stroke.Therefore, the currently existing assessment instruments were analyzed and it was found that the most used is the CAM-ICU, considering that it is not entirely suitable due to its limited capacity to explain an initial mental state that presents changes.The obstacles that make assessment most difficult are the neurological deficits present
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".