Body Mass Index, Diet-Related Factors, and Bladder Cancer Prognosis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Urologists are frequently confronted with questions of urinary bladder cancer (UBC) patients about what they can do to improve their prognosis. Unfortunately, it is largely unknown which lifestyle factors can influence prognosis. OBJECTIVE: To systematically review the available evidence on the association between body mass index (BMI), diet, dietary supplements, and physical activity and UBC prognosis. METHODS: We searched PubMed and Embase up to May 2017. We included thirty-one articles reporting on observational and randomized controlled trials investigating BMI, diet and dietary supplements in relation to recurrence, progression, cancer-specific or all-cause mortality in UBC patients. RESULTS: = 79%) were associated with increased risk of recurrence when compared to normal weight. No association of BMI with risk of progression was found. Results for BMI and prognosis in muscle-invasive or in all stages series were inconsistent. Observational studies on diet and randomized controlled trials with dietary supplements showed inconsistent results. No studies on physical activity and UBC prognosis have been published to date. CONCLUSIONS: Evidence for an association of lifestyle factors with UBC prognosis is limited, with some evidence for an association of BMI with risk of recurrence in NMIBC. Well-designed, prospective studies are needed to develop evidence-based guidelines on this topic.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".