Health-Related Quality of Life (HRQOL) reporting in phase III randomized controlled trials (RCTs) of metastatic prostate adenocarcinoma (mPCa) and urothelial carcinoma (mUC).
Bibliographic record
Abstract
478 Background: HRQOL outcomes are increasingly used to guide patient-centered care and health policy decisions, especially when treatment has a limited impact on survival. Our aim was to systematically evaluate the quality of HRQOL reporting in phase III RCTs of mPCa and mUC. Methods: A systematic literature review identified phase III RCTs evaluating palliative systemic therapies for castrate sensitive or castrate resistant mPCa (excluding androgen-deprivation therapy) and mUC, published in English between 1985 and 2018. Supplementary material and companion publications on HRQOL were also reviewed. RCTs reporting HRQOL outcomes were scored by the Minimum Standard Checklist for Evaluating HRQOL Outcomes in Cancer Clinical Trials (range 0-11). Results: Of 44 RCTs in mPCa, 25 (56.8%) reported HRQOL outcomes. BPI-SF or BPI (14 RCTs) and McGill-Melzack pain questionnaire (6 RCTs) were the most commonly used generic instruments, whereas FACT-P (16 trials), validated in advanced PCa, was the most commonly used disease-specific instrument. Average score of HRQOL reporting in mPCa was 6.7. Of 21 RCTs in mUC, 7 (33.3%) reported HRQOL. EORTC QLQ-C30 (6 RCTs) was the most commonly used generic instrument. Only 1 trial used a disease-specific instrument, FACT-BL, which lacks validation in mUC. Average score in mUC was 6.6. Conclusions: Robust HRQOL data from phase III RCTs in mPCa and mUC remain limited, and most HRQOL reports had methodologic shortcomings. Although the disease-specific FACT-P questionnaire being used is validated in advanced PCa, mUC RCTs have yet to use a culturally validated instrument to assess HRQOL. Quality of HRQOL reporting has improved over time but needs to be better harmonized across trials. [Table: see text]
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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 | 0.276 | 0.557 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".