The Richmond Agitation-Sedation Scale modified for palliative care inpatients (RASS-PAL): a pilot study exploring validity and feasibility in clinical practice
Bibliographic record
Abstract
BACKGROUND: The Richmond Agitation-Sedation Scale (RASS), which assesses level of sedation and agitation, is a simple observational instrument which was developed and validated for the intensive care setting. Although used and recommended in palliative care settings, further validation is required in this patient population. The aim of this study was to explore the validity and feasibility of a version of the RASS modified for palliative care populations (RASS-PAL). METHODS: A prospective study, using a mixed methods approach, was conducted. Thirteen health care professionals (physicians and nurses) working in an acute palliative care unit assessed ten consecutive patients with an agitated delirium or receiving palliative sedation. Patients were assessed at five designated time points using the RASS-PAL. Health care professionals completed a short survey and data from semi-structured interviews was analyzed using thematic analysis. RESULTS: The inter-rater intraclass correlation coefficient range of the RASS-PAL was 0.84 to 0.98 for the five time points. Professionals agreed that the tool was useful for assessing sedation and was easy to use. Its role in monitoring delirium however was deemed problematic. Professionals felt that it may assist interprofessional communication. The need for formal education on why and how to use the instrument was highlighted. CONCLUSION: This study provides preliminary validity evidence for the use of the RASS-PAL by physicians and nurses working in a palliative care unit, specifically for assessing sedation and agitation levels in the management of palliative sedation. Further validity evidence should be sought, particularly in the context of assessing delirium.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".