The Edmonton Symptom Assessment System, a proposed tool for distress screening in cancer patients: development and refinement
Bibliographic record
Abstract
OBJECTIVE: The Edmonton Symptom Assessment System (ESAS) has been proposed as one element of a distress screening strategy in cancer patients. It consists of 11-point numerical rating scales for self-report of nine common symptoms of cancer, with a 10th scale for a patient-specific symptom. The ESAS has undergone widespread adoption internationally for clinical, research and administrative purposes. Despite its rapid uptake, validity evidence has lagged behind, and concerns have been raised about feasibility and usefulness. The objective of this paper is to provide a synthesis of a program of research focusing on the psychometric properties of the ESAS. METHODS: We describe and discuss a series of three ESAS studies undertaken by our group: (i) a review of ESAS validation studies (1991-2006); (ii) a think-aloud study conducted in 20 advanced cancer patients; and (iii) a prospective multicenter study conducted in 160 patients in different palliative care settings, comparing the ESAS with a revised version (ESAS-r). RESULTS: The review identified 13 articles focusing on gathering reliability and/or validity evidence; the need to standardize the ESAS and conduct further validation research was apparent. The think-aloud study elucidated the complex cognitive processes by which patients arrive at symptom ratings and areas of potential difficulty in understanding and completing the ESAS. The multicenter study demonstrated that the ESAS-r was significantly easier for patients to understand. CONCLUSIONS: Overall, our findings support consideration of the ESAS and its successor, the ESAS-r, for use in distress screening in cancer patients. Areas for future research will be presented. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".