What can patient-reported experience measures tell us about the variation in patients’ experience of prostate cancer care? A cross-sectional study using survey data from the National Prostate Cancer Audit in England
Bibliographic record
Abstract
OBJECTIVES: A national survey aimed to measure how men with prostate cancer perceived their involvement in and decisions around their care immediately after diagnosis. This study aimed to describe any differences found by socio-demographic groups. DESIGN: Cross-sectional study of men who were diagnosed with and treated for prostate cancer. SETTING: The National Prostate Cancer Audit patient-reported experience measures (PREMs) survey in England. PARTICIPANTS: Men diagnosed in 2014-2016, with non-metastatic prostate cancer, were surveyed. Responses from 32 796 men were individually linked to records from a national clinical audit and to administrative hospital data. Age, ethnicity, deprivation and disease risk classification were used to explore variation in responses to selected questions. PRIMARY AND SECONDARY OUTCOME MEASURES: Responses to five questions from the PREMs survey: the proportion responding to the highest positive category was compared across the socio-demographic characteristics above. RESULTS: When adjusted for other factors, older men were less likely than men under the age of 60 to feel side effects had been explained in a way they could understand (men 80+: relative risk (RR)=0.92, 95% CI 0.84 to 1.00), that their views were considered (RR=0.79, 95% CI 0.73 to 0.87) or that they were involved in decisions (RR=0.92, 95% CI 0.85 to 1.00). The latter was also apparent for men who were not white (black men: RR=0.89, 95% CI 0.82 to 0.98; Asian men: RR=0.85, 95% CI 0.75 to 0.96) and, to a lesser extent, for more deprived men. CONCLUSIONS: The observed discrepancies highlight the need for more focus on initiatives to improve the experience of ethnic minority patients and those older than 60 years. The findings also argue for further validation of discriminatory instruments to help cancer care providers fully understand the variation in the experience of their patients.
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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 | 0.020 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".