Impact of enhanced optical techniques at time of transurethral resection of bladder tumour, with or without single immediate intravesical chemotherapy, on recurrence rate of non‐muscle‐invasive bladder cancer: a systematic review and network meta‐analysis of randomized trials
Bibliographic record
Abstract
OBJECTIVE: To assess whether single immediate intravesical chemotherapy (SIIC) adds value to bladder tumour management in combination with novel optical techniques: enhanced transurethral resection of bladder tumour (TURBT). METHODS: A systematic search was performed using the PubMed and Web of Science databases in September 2020 according to the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) extension statement for network meta-analyses. Studies that compared recurrence rates among intervention groups (TURBT with photodynamic diagnosis [PDD] ± SIIC, narrow-band imaging [NBI] ± SIIC, or white-light cystoscopy [WLC] + SIIC) and a control group (TURBT with WLC alone) were included. We used the Bayesian approach in the network meta-analysis. RESULTS: Twenty-two studies (n = 4519) met our eligibility criteria. Out of six different interventions including three different optical techniques, compared to WLC alone, blue-light cystoscopy (BLC) plus SIIC (odds ratio [OR] 0.349, 95% credible interval [CrI] 0.196-0.601) and BLC alone (OR 0.668, 95% CrI 0.459-0.931) were associated with a significantly lower likelihood of 12-month recurrence rate. In the sensitivity analysis, out of eight different interventions compared to WLC alone, PDD by 5-aminolevulinic acid plus SIIC (OR 0.327, 95% CrI 0.159-0.646) and by hexaminolevulinic acid plus SIIC (OR 0.376, 95% CrI 0.172-0.783) were both associated with a significantly lower likelihood of 12-month recurrence rate. NBI with and without SIIC was not associated with a significantly lower likelihood of 12-month recurrence rate (OR 0.385, 95% CrI 0.105-1.29 and OR 0.653, 95% CrI 0.343-1.15). CONCLUSION: Blue-light cystoscopy during TURBT with concomitant SIIC seems to yield superior recurrence outcomes in patients with non-muscle-invasive bladder cancer. The use of PDD was able to reduce the 12-month recurrence rate; moreover, concomitant SIIC increased this risk benefit by a 32% additional reduction in odds ratio. Although using PDD could reduce the recurrence rate, SIIC remains necessary. Moreover, ranking analysis showed that both PDD and NBI, plus SIIC, were better than these techniques alone.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.041 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".