Rasch analysis of the Edmonton Symptom Assessment System and research implications
Bibliographic record
Abstract
BACKGROUND: Reliable and valid assessment of the disease burden across all forms of cancer is critical to the evaluation of treatment effectiveness and patient progress. The Edmonton Symptom Assessment System (esas) is used for routine evaluation of people attending for cancer care. In the present study, we used Rasch analysis to explore the measurement properties of the esas and to determine the effect of using Rasch-proposed interval-level esas scoring compared with traditional scoring when evaluating the effects of an exercise program for cancer survivors. METHODS: Polytomous Rasch analysis (Andrich's rating-scale model) was applied to data from 26,645 esas questionnaires completed at the Juravinski Cancer Centre. The fit of the esas to the polytomous Rasch model was investigated, including evaluations of differential item functioning for sex, age, and disease group. The research implication was investigated by comparing the results of an observational research study previously analysed using a traditional approach with the results obtained by Rasch-proposed interval-level esas scoring. RESULTS: The Rasch reliability index was 0.73, falling short of the desired 0.80-0.90 level. However, the esas was found to fit the Rasch model, including the criteria for uni-dimensional data. The analysis suggests that the current esas scoring system of 0-10 could be collapsed to a 6-point scale. Use of the Rasch-proposed interval-level scoring yielded results that were different from those calculated using summarized ordinal-level esas scores. Differential item functioning was not found for sex, age, or diagnosis groups. CONCLUSIONS: The esas is a moderately reliable uni-dimensional measure of cancer disease burden and can provide interval-level scaling with Rasch-based scoring. Further, our study indicates that, compared with the traditional scoring metric, Rasch-based scoring could result in substantive changes to conclusions.
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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 | 0.071 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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".