Testing a modified electronic version of the Edmonton symptom assessment system-revised for remote online completion with ambulatory cancer patients in Alberta, Canada
Bibliographic record
Abstract
Objective: The cancer program in Alberta, Canada routinely collects patient-reported outcomes using the Edmonton symptom assessment system-revised (ESAS-r). The program recently launched the province's new clinical information system which has expanded functionality, allowing patients to complete symptom questionnaires remotely online, instead of completing a paper form at the clinic. This study aimed to test a modified electronic version of the ESAS-r [(e)ESAS-r] with patients, to assess the feasibility of completion and questionnaire clarity. Methods: Staff, patients, and other stakeholders worked to create modified definitions for ESAS-r symptoms, to aid in patient understanding. Patient and family advisors were recruited to test the questionnaire. Participants completed an online mock-up of the (e)ESAS-r and answered questions about technical issues. One-to-one cognitive interviews were held to discuss each symptom definition in detail. Modifications were made based on the feedback and a second round of interviews was held to finalize the wording. Results: In total, 19 patients and 7 family advisors participated. All but one (96.2%) completed the questionnaire without assistance and had no technical issues. Participants requested certain wording modifications and that definitions be added for all symptoms for consistency. Very few participants reported any confusion with the final definitions. Conclusions: The (e)ESAS-r was tested for clarity and ease of completion and was determined to be suitable for remote online use with ambulatory cancer patients. The enhanced definitions on the new questionnaire were clear to patients and helped ensure they understood the meaning of each symptom they were asked to rate.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".