Blue vs white light for transurethral resection of non‐muscle‐invasive bladder cancer: an abridged Cochrane Review
Bibliographic record
Abstract
OBJECTIVES: To assess the effects of blue-light (BL)-enhanced transurethral resection of bladder tumour (TURBT) compared to white-light (WL)-based TURBT in the treatment of non-muscle-invasive bladder cancer (NMIBC). METHODS: Based on a published protocol, we performed a systematic search of multiple databases from their inception to March 2021. We included randomized controlled trials (RCTs) comparing blue-light (BL) TURBT to white-light (WL) TURBT. Our meta-analysis was based on a random-effects model. We assessed the quality of evidence on a per-outcome basis according to the Grades of Recommendation, Assessment, Development and Evaluation (GRADE) approach. RESULTS: We included 16 RCTs involving a total of 4325 participants in this review. BL TURBT may reduce the risk of disease recurrence over time (hazard ratio [HR] 0.66, 95% confidence interval [CI] 0.54-0.81; low-certainty evidence) depending on baseline risk. For participants with low-, intermediate- and high-risk NMIBC, this corresponded to 48 (66 fewer to 27 fewer), 109 (152 fewer to 59 fewer) and 147 (211 fewer to 76 fewer) fewer recurrences per 1000 participants when compared to WL TURBT, respectively. BL TURBT may also reduce the risk of disease progression over time (HR 0.65, 95% CI 0.50-0.84; low-certainty evidence) depending on baseline risk. For participants with low-, intermediate- and high-risk NMIBC, this corresponded to 1 (1 fewer to 0 fewer), 17 (25 fewer to 8 fewer), and 56 (81 fewer to 25 fewer) fewer progressions per 1000 participants when compared to WL TURBT, respectively. CONCLUSIONS: Our findings suggest a favourable impact of BL TURBT on the risk of disease recurrence and progression; however, whether this risk reduction is clinically relevant greatly depends on the baseline risk of patients. We did not find an increase in severe surgical complications with BL cystoscopy, and we did not find any trial evidence on other, non-surgical adverse events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".