Comparison of Patient-Reported Experience of Patients Receiving Radiotherapy Measured by Two Validated Surveys
Bibliographic record
Abstract
Patient-reported experience is associated with improved patient safety and clinical outcomes. Quality improvement programs rely on validated patient-reported experience measures (PREMs) to design projects. This descriptive study compares the experience of cancer patients treated with radiation as recorded through the Ambulatory Oncology Patient Satisfaction Survey (AOPSS) or as recorded through Your Voice Matters (YVM) between February and August 2019. Six questions were compared ("overall experience with care", "discussion of worries", "involvement in decisions", "trusting providers with confidential information", "providing family with information", and "knowing who to contact"). Positive experience scores were calculated by cohort and by tumor groups. Multivariable logistic regression models evaluated factors associated with positive experience. Two cohorts (220 and 200 patients) met the eligibility criteria for the AOPSS and YVM, respectively. Positive experience was reported similarly between the two PREMs for "overall experience with care", "discussion of worries", and "trusting providers with confidential information" with a score difference of 1-4% at the cohort level. Positive experience score difference ranged from 5% to 44% across questions at the tumor group level. Different experience gaps were identified with the two measures, mainly at the tumor group level. Programs interested in using these PREMS might consider this when designing projects.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".