Effect of family reorientation messages on delirium prevention among critically ill patients
Bibliographic record
Abstract
Background: About 50%-80% of critically ill patients develop delirium during their intensive care unit (ICU) stay. Adverse events associated with delirium can range from functional disability, cognitive and psychological impairment, dementia and even death. Removal of invasive lines, self-extubation, prolonged sedation and ventilation therapies which delay the ICU liberation, and increase the overall hospital length of stay are also negative squeals of delirium. Delirium has series of adverse events that are not limited to the associated morbidies and mortality, but also extended to include the burden placed on caregivers, families and healthcare services, in addition to increasing the cost of care. Using auditory stimulation as a non-pharmacological intervention can stimulate the affected neural networks, accelerate brain plasticity and avoid sensory deprivation that could induce pain, agitation, and delirium and slow down the patients' recovery. It is evident that familiar auditory stimuli by a familiar voice is eliciting more responses to auditory tones as it can grasp patients' attention without much effort and disrupts ongoing cognitive activities. Accordingly, multicomponent family reorientation strategy has recently been proposed to achieve better outcomes.Methods: A quasi experimental research design was used in this study in which one tool was used for data collection: “Confusion Assessment Method-intensive care unit (CAM-ICU)”. Results: During the five-day intervention period, the delirium free days was all the days in the family voice group, four days in the unfamiliar voice group and no free days in the control group which indicates a significant difference among groups on number of delirium free days (MCp < .001*).Conclusion: Reorienting critically ill patients through recorded messages is an effective strategy to reduce the incidence of delirium. Furthermore, using a familiar family sound is more effective in reducing delirium as proved by the number of delirium free days. During the five-day intervention period, the family voice group shows more delirium free days than the unfamiliar voice group. The intervention used in this study is easy, costless and effective strategy in prevention of delirium among critically ill patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".