Patient Experiences Regarding Feasibility of Implementing Real-World EQ-5D Collection at an Oncology Centre in Ontario, Canada
Bibliographic record
Abstract
Cancer treatments impact health-related quality of life (HRQoL). EQ-5D is a standardized generic measure of HRQoL. The objective of this project was to assess, from the patient's perspective, the feasibility of implementing real-world EQ-5D-3L measurement at a pilot site, as a first step to large-scale collection of EQ-5D from patients with cancer across Ontario. This was a prospective longitudinal study at a single oncology centre to understand the feasibility of routinely collecting EQ-5D-3L while patients receive chemotherapy (N = 170). Consenting patients completed an additional questionnaire on feasibility, and a subset of participants were directly interviewed to provide further feedback and suggest improvements to questionnaire collection. Themes emerging from the interviews were analyzed using content analysis. Of 170 eligible and consenting patients who completed an initial EQ-5D-3L questionnaire, 103 (60.6%) completed at least one follow-up questionnaire. When asked about willingness to answer future questionnaires at subsequent visits, 115 (67.3%) answered definitely; 35 (20.5%) very likely. Patients provided feedback on their overall experience of completing EQ-5D-3L, the questionnaire presentation, frequency of completion, and analytic plans. Patients expressed that routinely collecting EQ-5D-3L is feasible. Incorporating patient feedback regarding EQ-5D collection will facilitate implementation of systematic collection at cancer centres across Ontario.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 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".