Does physical function impact the acceptability of routine patient-reported outcome surveys in cancer patients?
Bibliographic record
Abstract
67 Background: Patient-reported outcome measures (PROMs) are used to collect cancer-related symptom and symptomatic toxicity data. Understanding factors that affect the acceptability of PROMs is useful for improving the participation of such routine screening practices. We investigated the association between patient’s functional status on their acceptance and perception of usefulness of tablet technology to routine collection of PROM data in the clinical setting. Methods: 279 adult cancer outpatients at Toronto’s Princess Margaret Cancer Centre completed two physical function surveys, the HAQ-DI and WHODAS, using touchscreen tablet technology. Patient socio-demographics and their willingness of completing PROMs on a routine basis were also collected. Results: The median age was 62 (range: 20–94); 50% male, and 68% Caucasian. Patients were recruited from breast (10.4%), gastrointestinal (10.4%), genitourinary (14%), gynecology (14.3%), head & neck (17.9%), hematology (17.6%), and thoracic (15.4%) cancer clinics. 87% were willing to complete the survey on a touchscreen tablet, 74% did not find it time-consuming, and 4% found completion of the survey made their clinic visit more difficult. Only 1% found the questions upsetting or distressful; however, less than 60% were willing to complete similar surveys at every visit. Being Caucasian (p = 0.015) and Canadian-born (p = 0.021) were significantly associated with acceptability of the survey on a regular basis. However, HAQ-DI and WHODAS are not significantly associated with willingness to complete PROMs regularly (p = 0.30 & p = 0.16 respectively). Conclusions: Physical function status is not associated with acceptability; however, ethnicity does play a role where Canadian-born and Caucasian patients are more likely to complete PROMs regularly. Despite this, over 40% of patients were not willing to fill in the PROMs at each visit. Socio-cultural factors may impact on patients’ perceptions of the value of routine screening; however, assessment burden may also limit willingness to complete surveys on an ongoing basis. Further research is underway to gain more insight into the limiting factors of patients to complete the survey routinely.
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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 | 0.015 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".