Smoking and bladder cancer: review of the recent literature
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review summarizes the current knowledge regarding the relationship between smoking and bladder cancer (BCa), especially with respect to treatment outcomes for muscle and nonmuscle-invasive BCa (MIBC/NMIBC). RECENT FINDINGS: PubMed/Medline databases were searched for recent reports investigating the association of smoking with BCa. Smoking is associated with an increased risk of recurrence in patients with NMIBC and may impair Bacillus Calmette-Guerin treatment efficacy. Moreover, smoking is associated with poor responses to neoadjuvant chemotherapy, poor survival outcomes and high complication rates in patients undergoing radical cystectomy. Smoking cessation mitigates these negative effects, especially. However, the amount of patient counselling provided regarding this important matter and patient knowledge regarding smoking and BCa risk are inadequate. Currently, the impact of secondhand smoke on BCa risk remains uncertain. SUMMARY: Tobacco smoking is responsible for approximately half of BCa cases, and is associated with poor oncological outcomes for both NMIBC and MIBC. Despite smoking being a well known risk factor, counselling and knowledge in this area are insufficient. Appropriate smoking cessation interventions and patient information are required to improve patient health and optimize BCa survival.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".