Feasibility of collecting survey-based patient-reported outcome measures (PROMs) from patients living with advanced cancer: Emerging findings from the Living with Colorectal Cancer study.
Bibliographic record
Abstract
207 Background: The “Living with Colorectal Cancer study” seeks to characterize the experiences of patients diagnosed with advanced colorectal cancer to inform care improvements. Here we describe our experiences recruiting patients and collecting patient reported outcome measures (PROMs). Methods: Eligible patients were identified by oncologists in Alberta, Canada’s two tertiary cancer centres and approached for consent to participate during routine appointments. Following baseline surveys, participants were given a choice of completing monthly surveys via email, phone, post, or in person. We purposively chose previously validated surveys already in use provincially, including EQ-5D-5L and Putting Patients First. We endeavoured to include non-English speaking participants by providing translated study materials and interpretation. Results: In one year of recruitment, 88 patients were enrolled. Edmonton, Alberta’s patient recruitment (N = 62) is double that of Calgary, Alberta (N = 26), despite similar population sizes. In Calgary and Edmonton, 81% and 56% of participants chose email-based PROMs surveys, respectively. The current missing survey rate is 12% (i.e. surveys not completed per month). Forty-eight participants (55%) completed ≥6 sets of monthly PROMs data; 26 (30%) transferred “off study” (61% of which died). Several participants expressed the desire to describe their experiences beyond what the surveys could accommodate. Despite language accommodation, all participants chose to complete surveys in English; however, 21% reported speaking another language daily. Conclusions: It is challenging but possible to engage and retain patients with advanced cancer in research focused on PROMs. Speaking with patients face-to-face in cancer clinics increased the burden on healthcare providers (i.e. clinic flow and time spent with patients), but appears to be a practical and appropriate way to recruit participants. Offering multiple methods of communication allowed patients to participate in a manner most practical for their lifestyle and did not impact PROMs collection. Clinical trial information: NCT03572101.
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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 | 0.058 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".