Improving the Management of Terminal Delirium at the End of Life
Bibliographic record
Abstract
OBJECTIVE: Terminal delirium is a distressing process that occurs in the dying phase, often misdiagnosed and undertreated. A hospital developed the "comfort measures order set" for dying patients receiving comfort care in the final 72 h of life. A chart review of patients experiencing terminal delirium revealed that the current medication option initially included in the order set was suboptimally effective and patients with terminal delirium were consistently undertreated. The purpose of this pilot study was to highlight an in-service intervention educating nurses on the management of terminal delirium at the end of life and to assess its effect on their knowledge of the management of patients with terminal delirium. METHODS: A before-and-after survey design was used to assess the effect of the in-service training on nurses' knowledge of terminal delirium. RESULTS: We describe the results from a small sample of nurses at a large urban tertiary care center in Canada. Of the twenty nurses who attended the in-services, 60% had cared for a patient with terminal delirium; however, 50% felt that their knowledge of the topic was inadequate. Despite no statistical significance between the pre- and posttest scores for both the oncology and the medicine unit nurses, all participants who completed posttest survey found the in-services useful. CONCLUSIONS: The findings from this study provide initial insights into the importance of in-service trainings to improve the end-of-life care and nursing practice. Future research will include expanding this pilot project with sufficient power to assess the significance of these types of interventions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".