Bladder volume reproducibility after water consumption in patients with prostate cancer undergoing radiotherapy: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: To minimize toxicity due to radiotherapy in patients with prostate cancer, high bladder volume reproducibility is essential. Water consumption is often used to increase bladder volume reproducibility, but the optimal amount of water required to be consumed remains unclear. We aimed to analyzed the relationship between water consumption and bladder volume reproducibility in patients undergoing radiotherapy for prostate cancer. METHODS: We conducted a systematic review and meta-analysis of randomized controlled trials and cohort studies that assessed bladder volume change after water consumption in patients with prostate cancer undergoing radiotherapy. MEDLINE, Embase, and Cochrane Central Register of Controlled Trials were searched for relevant studies published from database inception up until July 4, 2020. The Newcastle-Ottawa Scale was used to evaluate the risk of bias in the included studies. The outcome was the mean difference (MD) of bladder volume after water consumption, evaluated through meta-analysis using a random-effects model. RESULTS: Ten cohort studies and one randomized controlled trial with a total of 417 patients were included. For 300-400 ml water consumption, the bladder volume MD between during treatment and at computer tomography-simulation (95% confidence interval [CI]) was -11.97 (-51.68 to 27.74), was -45.99 (-82.85 to -9.13) for 500-540 ml water consumption and -45.92 (-78.86 to -12.98) for water consumption until full-bladder sensation was reached. CONCLUSION: Consuming 300-400 ml of water potentially leads to the best bladder volume reproducibility; moreover, the higher the water consumption volume, the lower the bladder volume reproducibility.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.049 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".