Delirium: The 21st century health care challenge for bedside clinicians
Bibliographic record
Abstract
Delirium is a leading cause of preventable injury in hospitalized patients. Early recognition and intervention for delirium are critical to prevent morbidity and mortality, especially in the older population. Older patients are at increased risk for delirium owing to a combination of age-related changes and environmental factors. Health care providers, including nurses and physicians, often miss delirium symptoms and diagnosis in patients. Without early recognition and treatment, delirium can have significant life-changing consequences in our most vulnerable patients. This acute change in cognition can continue throughout the hospital course and may require additional rehabilitation or placement, delaying transition to home. As the baby boomers age, the older population is expected to increase, with significant implications for health care. With this in mind, the health care team, including frontline caregivers, need to be well informed about delirium. This article will expand readers’ knowledge and familiarity with delirium with the purpose of improving their practice and care of the older patient. It will also address the impact of delirium and discuss tools that can help to improve recognition. The most recent advances and current treatment methods to integrate into daily patient care are also discussed. This article places heavy emphasis on identification and prevention of delirium as these are the most important aspect of understanding delirium. Thus, treatment and management are both discussed after prevention since the primary focus of delirium is understanding and preventing this devastating syndrome in our hospitalized 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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.028 | 0.034 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".