The detection of delirium in the ICU: An important aspect of care
Bibliographic record
Abstract
Normal 0 false false false EN-CA X-NONE X-NONE Background: Up to 80% of mechanically ventilated ICU patients develop delirium. Unfortunately, delirium is under- estimated and thus, under-recognized by health care’s providers. The use of a delirium screening tool has been proven efficient to promptly recognize patients suffering of delirium and to quickly intervene. Also, educational activities have been proven essential to ensure appropriate prevention, identification and treatment of delirium. Methodology: The goal of this innovative project was to develop and implement an educational activity on delirium to facilitate the implementation of the Richmond Agitation-Sedation Scale (RASS) and Confusion Assessment Method for the ICU (CAM-ICU) in the ICU of a Canadian university teaching hospital. The educational activity consisted in a 45 minutes teaching session on delirium, the RASS and the CAM-ICU scales, which was followed by a six weeks mentoring of nurses and clinical follow-up of ICU patients. The project was led by an undergraduate nursing student as part of an internship and supervised by the Intensive Care Unit’s Clinical Nurse Specialist and Nursing Education Consultant. Principal findings: A total of 80 nurses and many members of the interdisciplinary team benefited from the educational activity. In addition, nursing expertise in screening for delirium was recognized by the multidisciplinary team and delirium was systematically addressed during the daily medical rounds. The student leading the project was providing support, guidance and mentoring as needed. As reported by the staff, the support received facilitated the successful implementation of the delirium screening tool, the Confusion Assessment Method for the ICU (CAM-ICU). In addition, it helped the team members to implement interventions to prevent and treat delirium that were tailored to the patient’s needs. Conclusion: Early detection of delirium allows the initiation of prompt treatment thus reducing its negative impact and improving quality of care. Educational activities are important to help achieve these standards.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".