Rasch Analysis of the Edmonton Symptom Assessment System
Bibliographic record
Abstract
CONTEXT: The Edmonton Symptom Assessment System (ESAS) is a widely used multisymptom assessment tool in cancer and palliative care settings, but its psychometric properties have not been widely tested using modern psychometric methods such as Rasch analysis. OBJECTIVES: To apply Rasch analysis to the ESAS in a community palliative care setting and determine its suitability for assessing symptom burden in this group. METHODS: ESAS data collected from 229 patients enrolled in a community hospice service were evaluated using a partial credit Rasch model with RUMM2030 software (RUMM Laboratory Pty, Ltd., Duncraig, WA). Where disordered thresholds were discovered, item rescoring was undertaken. Rasch model fit and differential item functioning were evaluated after each iterative phase. RESULTS: = 29.56 [27]; P = 0.33) that permitted ordinal-to-interval conversion. CONCLUSION: The ESAS satisfied unidimensional Rasch model expectations in a 12-item format after minor modifications. This included uniform rescoring of the disordered response categories and creating superitems to improve model fit and clinical utility. The accuracy of the ESAS scores can be improved by using ordinal-to-interval conversion tables published in the article.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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".