What does the answer mean? A qualitative study of how palliative cancer patients interpret and respond to the Edmonton Symptom Assessment System
Bibliographic record
Abstract
The Edmonton Symptom Assessment System (ESAS) is a well-known self-reporting tool for symptom assessment in palliative care. Research has shown that patients experience difficulties in the scoring and interpretation, which may lead to suboptimal treatment. The aims were to examine how palliative care cancer patients interpreted and responded to the ESAS. Eleven patients (3 F/8 M), median age 65 (34-95) with mixed diagnoses were interviewed by means of cognitive interviewing, immediately after having completed the ESAS. The highest mean scores were found with tiredness (6.3) and oral dryness (5.7). The results showed that sources of error were related to interpretation of symptoms and differences in the understanding and use of the response format. The depression and anxiety symptoms were perceived as difficult to interpret, while the appetite item was particularly prone to misunderstandings. Contextual factors, such as mood and time of the day, influenced the answers. Lack of information and feedback from staff influenced the scores. Some patients stated that they scored at random because they did not understand why and how the ESAS was used. The patients' interpretation must be considered in order to minimize errors. The ESAS should always be reviewed with the patients after completion to improve symptom management, thereby strengthening the usability of the ESAS.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".