Improving quality of health-related quality of life (HRQOL) reporting in phase III randomized controlled trials (RCTs) of metastatic urothelial carcinoma (mUC).
Bibliographic record
Abstract
467 Background: HRQOL data are increasingly used to guide patient care and health policy. However, their utility can be compromised by inadequate quality of HRQOL reporting (QHR). We aimed to systematically evaluate QHR in phase III RCTs of mUC. Methods: A comprehensive systematic review following PRISMA guidelines identified published manuscripts of phase III RCTs of mUC in English between 1985 - 2019. Supplementary material, references and companion publications were reviewed. QHR was quantified using: 1) 2013 CONSORT-PRO extension (CPE), and 2) 2003 Minimum Standard Checklist for Evaluating HRQOL Outcomes in Cancer Clinical Trials (MSC). Both scores range from 0-11, with higher scores indicating higher QHR. QHR is “probably robust” if MSC score is ≥8 and all 3 mandatory items (baseline compliance, missing data, and psychometric properties) are reported; “limited” if MSC score is 5-7; and “very limited” if MSC score is ≤4. Results: HRQOL data was collected in 7/21 (33%) mUC phase III RCTs. All reported HRQOL data as secondary/exploratory endpoints. Generic instruments included EORTC QLQ-C30 alone (n=3) or combined with EQ-5D (n=2)/McGill pain questionnaire (n=1). Disease-specific FACT-Bl was used in 1 RCT. Both checklists showed strong correlation (Spearman coefficient 0.94, p=0.001). QHR was often limited, however has improved in recent years (Table). No RCT stated HRQOL-specific hypotheses. Only 1 reported instrument validity and mode of administration. Few provided domain specific results (n=2) and statistical methods for handling missing data (n=3). Implications for clinical practice were only discussed in 3 RCTs. Conclusions: QHR has improved over time however many critical methodological deficiencies remain in mUC phase III RCTs. The use of standard checklists are encouraged to further enhance QHR and promote high quality HRQOL research. [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 | 0.434 | 0.602 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".