A Systematic Review of Outcome Reporting, Definition and Measurement Heterogeneity in Non-Muscle Invasive Bladder Cancer Effectiveness Trials of Adjuvant, Prophylactic Treatment After Transurethral Resection
Bibliographic record
Abstract
BACKGROUND: Heterogenous outcome reporting in non-muscle-invasive bladder cancer (NMIBC) effectiveness trials of adjuvant treatment after transurethral resection (TURBT) has been noted in systematic reviews (SRs). This hinders comparing results across trials, combining them in meta-analyses, and evidence-based decision-making for patients and clinicians. OBJECTIVE: We aimed to systematically review the extent of reporting and definition heterogeneity. METHODS: ) published between 2000-2020. Abstracts and full texts were screened independently by two reviewers. Data were extracted by one reviewer and checked by another. RESULTS: We screened 807 abstracts; from 15 SRs, 57 RCTs were included. Verbatim outcome names were coded to standard outcome names and organised using the Williamson and Clarke taxonomy. Recurrence (98%), progression (74%), treatment response (in CIS studies) (40%), and adverse events (77%) were frequently reported across studies. However, overall (33%) and cancer-specific (33%) survival, treatment completion (17%) and treatment change (37%) were less often reported. Quality of Life (3%) and economic outcomes (2%) were rarely reported. Heterogeneity was evident throughout, particularly in the definitions of progression and recurrence, and how CIS patients were handled in the analysis of studies with predominantly papillary patients, highlighting further issues with the definition of recurrence and progression vs treatment response for CIS patients. Data reporting was also inconsistent, with some trials reporting event rates at various time-points and others reporting time-to-event with or without Hazard Ratios. Adverse events were inconsistently reported. QoL data was absent in most trials. CONCLUSIONS: Heterogenous outcome reporting is evident in NMIBC effectiveness trials. This has profound implications for meta-analyses, SRs and evidence-based treatment decisions. A core outcome set is required to reduce heterogeneity. PATIENT SUMMARY: This systematic review found inconsistencies in outcome definitions and reporting, pointing out the urgent need for a core outcome set to help improve evidence-based treatment decisions.
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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 | 0.132 | 0.391 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.016 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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; 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".