The use of the edmonton symptom assessment scale to measure symptoms at the end of life
Bibliographic record
Abstract
Aims There is currently no tool available to capture symptom control in the dying. The Edmonton Symptom Assessment Scale (ESAS) has been validated for patient and care giver-rated scoring of patients' symptoms, enabling recording to continue as patients deteriorate. However, it has not been used specifically to measure symptoms in the last days of life. The aim was to assess the utility of ESAS in scoring symptoms in the dying patient as measured by completion rates in a ‘before and after’ study investigating the impact of an end-of-life (EOL) care tool. Methods 70 patients were recruited; 40 before and 30 following the introduction of the EOL tool. Nursing staff were asked to complete 12-hourly ESAS scores from the time the ‘diagnosis of dying’ was made until death. Results 54% of the ESAS forms were completed but some were not fully scored. 77% of patients recruited had an ESAS form completed in the last 24 h of life. The number of patients with a score for each of the individual symptoms within ESAS varied in both groups. Core EOL symptoms (pain, shortness of breath, nausea, chest secretions and agitation) were scored more frequently when the EOL tool was in place. The frequency of completion of these core symptoms ranged from 75–97% for patients receiving care directed by the EOL tool versus 28–58% for patients receiving usual care. Conclusion The proportion of missing data suggests 12 hourly symptom scoring was onerous for ward staff. The improved completion rate for core EOL symptoms suggests either that the introduction of the tool improved the nurses' ability or confidence to measure symptoms, or that scoring improved because the task was ‘learnt’ during the study. A shortened ESAS containing only core EOL symptoms might have better utility as an outcome measure for the last days of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".