Improving quality of health-related quality of life (HRQOL) reporting in phase III randomized controlled trials (RCTs) of metastatic prostate cancer (mPC).
Bibliographic record
Abstract
58 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 evaluate QHR in phase III RCTs of mPC. Methods: A systematic review following PRISMA guidelines identified published manuscripts of phase III RCTs of mPC assessing systemic therapies (excluding androgen deprivation therapy and bone targeting agents) between 1990 - 2019. Supplements, references and companion publications were reviewed. QHR was independently quantified using the Minimum Standard Checklist for Evaluating HRQOL Outcomes in Cancer Clinical Trials (MSC, range 0-11) by 2 investigators. 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 score is 5-7; and “very limited” if score is ≤4. Results: HRQOL was the primary (11%), secondary (61%), explorative (21%), and unspecified (7%) endpoint in 57/76 (75%) RCTs, and reported in 46/57 (81%). Primary HRQOL endpoints were pain palliation only. MSC scores ranged 2 – 11. QHR was mostly limited (Table). Most RCTs did not report mode of administration (82%), rationale for selected instrument (65%), or missing data (59%). Other common limitations were: unreported baseline compliance (35%), lack of culturally validated measure (33%), clinical significance not discussed (33%), no hypotheses stated (28%), and inadequate coverage of HRQOL domains (26%). QHR has improved since 2015, with the median MSC score reaching 8 (Table). Conclusions: QHR has improved considerably over time, with many recent phase III RCTs in mPC reporting “probably robust” HRQOL data. Within existing resource constraints however, nonreporting and methodologic deficiencies still remain. The use of standard checklists may 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 | 0.540 | 0.679 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.016 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".