Demographic analysis of randomized controlled trials in bladder cancer
Bibliographic record
Abstract
UNLABELLED: WHAT'S KNOWN ON THE SUBJECT? AND WHAT DOES THE STUDY ADD?: Results from well designed randomized controlled trials usually provide the strongest evidence possible in favour of one medical intervention over another. For this reason, it is of paramount importance to conduct such trials in bladder cancer, where randomized trials are lacking, in particular to answer questions that have so far confounded us or to investigate the efficacy of new diagnostic tools or interventions. This study provides a demographic analysis of randomized controlled trials published in bladder cancer between the years of 1995 and 2010, with only 238 articles identified. Less than one-third of these reported a statistical power calculation, and only 8% were double-blinded. With many publications inaccurately labelled as randomized trials, we reveal the scarcity of trials performed over the given time period, even compared with other cancers with similar incidence, and highlight the need for more well designed trials to be conducted. OBJECTIVE: To demographically examine randomized controlled trials (RCTs) that have been conducted in bladder cancer over a predefined time period. METHODS: Various techniques have been described to detect RCTs using different databases. We searched the MEDLINE database by crossing the heading 'Urinary bladder neoplasms' with the MeSHs 'Clinical trial$.mp. OR clinical trial.pt. OR random:.mp. OR tu.xs.' between 1995 and 2010. For the RCTs identified, analysis was performed on each RCT, placing particular emphasis on modality of intervention, cohort size, principal author, region, journal type, disease status, histology, blinding, number of centres involved, performance of a statistical power calculation, accrual status and trial support. RESULTS: Of 5002 RCT bladder cancer papers retrieved over the given period, only 238 represented actual RCTs after manual appraisal. More than half of the RCTs investigated medical and surgical therapies (54.2%), and only half had a sample size of >100 patients. A small percentage of studies were double-blinded (8.0%), and there was an almost equal distribution of multicentre vs single centre trials (54.6% vs 45.4%). More studies were conducted in Europe (61.3%) than the rest of the world combined, with urologists principally the lead investigators in the majority (72.3%). Most studies were conducted on patients with urothelial carcinoma (97.1%), with less than one-third reporting a statistical power calculation (31.5%). CONCLUSIONS: Only 238 RCTs were published for bladder cancer between 1995 and 2010. RCTs are under-utilized in bladder cancer. More trials need to be designed with larger sample sizes in order to optimize diagnostic and treatment strategies for patients with bladder cancer.
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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 | 0.315 | 0.680 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".