The Edmonton Symptom Assessment System: a 15-year retrospective review of validation studies (1991–2006)
Bibliographic record
Abstract
INTRODUCTION: The purpose of this review was to identify and critique validation studies focusing on the Edmonton Symptom Assessment System (ESAS), a commonly used symptom assessment tool for advanced cancer and palliative patients. METHODS: Using a comprehensive literature search, the authors identified and screened 87 publications. Thirteen articles were selected for in-depth review, based on the following inclusion criteria: psychometric studies with a primary focus on the ESAS, 1991--2006 publication dates and peer-reviewed English language publications. RESULTS: Most studies involved cancer patients (n = 11). The ESAS format varied across studies, in terms of scale format, item number, item selection and language. Studies focused on gathering reliability estimates (n = 8), content validity evidence (n = 1), concurrent validity evidence (n = 5), predictive validity evidence (n = 1), and sensitivity and/or specificity (n = 3). None of these studies involved patients' perspectives as a source of validity evidence. DISCUSSION: The use of varying instrument formats and limited psychometric evidence support the need for further ESAS validation studies, including the involvement of patients.
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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 | 0.021 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".