Are palliative patients less accepting to self-report symptom measures for clinical management than curative patients?
Bibliographic record
Abstract
79 Background: Patient reported outcome measures (PROMs) are a tool used for collecting patient symptom data in the clinic, prior to the patient’s appointment. Patients being treated with a palliative intent are often assumed to find completing PROM surveys more burdensome than patients being treated with a curative intent. We compared patient acceptability of palliative versus curative patients to complete PROM surveys. Methods: 202 cancer out-patients (120 curative and 82 palliative) at the Princess Margaret Cancer Centre completed a PROM survey using a touchscreen tablet regarding their symptoms of pain. Ten questions assessed patient acceptance of completing PROM surveys. Results: The median age was 60 (range 21-86) and 48% were female. There were no clinically relevant demographic differences between the palliative and curative patients; however there were a higher proportion of palliative patients than curative patients with gynecological (8.3% palliative, 16% curative), lung (5.8% palliative, 10% curative), and gastrointestinal (14% palliative, 28% curative) cancers. There were no significant differences in acceptability between palliative and curative patients (p>0.05, all 10 comparisons). Only 3.6% of palliative patients and 3.3% of curative patients reported that completing the survey made their clinic visit more difficult; 16% of palliative patients and 11% of curative patients found the survey to be time consuming; no palliative patients and only 0.83% of curative patients found the questions upsetting or distressful. 93% of palliative patients and 92% of curative patients were happy to complete the surveys on a touchscreen tablet. Overall 62% of patients surveyed were willing to complete surveys at every visit (56% palliative and 67% curative, p>0.05). Conclusions: Palliative and curative patients appear to have an equally high level of acceptance of PROM surveys, though a significant minority would have problems with completing them at every visit. Further research using mixed-methods analysis will be done to better understand the factors limiting the overall willingness of both palliative and curative patients to complete the survey on a regular basis.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".