The role of urologists in smoking cessation: what they can do for urological cancer patients
Bibliographic record
Abstract
Introduction Tobacco use is the most preventable cause of death, including death from cancer. Smoking is a well-known risk factor for various cancers, including bladder cancer (BCa). BCa is the most common malignancy of the urinary tract, the seventh most common cancer in men worldwide, and eighth most frequent cause of cancer-specific mortality in Europe. Smoking has been well documented as a risk factor for BCa,incidence and the factor that worsens BCa treatment outcomes and prognosis. Study Aim To evaluate the need and a potential of smoking cessation interventions that urologists can perform in their medical practice for patients with cancer of urinary or urogenital tract. Methods A search of recent literature was conducted using the MEDLINE data base and the Internet, as well as resources from well- known health, cancer and tobacco control organizations. Results Smoking cessation is proved to be one of the most effective primary and secondary preventive methods in cancer patients, however, in medical practice is still used in limited extent. Among cancer patients is mostly advised to those with lung, laryngeal or oral cancer. National health surveys show that even general practitioners (GPs) do not advise and assist their patients in quitting smoking on regular basis (such advice is usually provided to only 40-50% of smoking patients). Urologists tend to do it in medical practice much less often than GPs. A large study that examined the practice patterns of American urologists, including their smoking cessation assistance for patients with BCa, showed that over half of urologists never discussed smoking cessation and only one of five always had a talk with his BCa patient on smoking cessation. It mainly resulted from big loopholes in their knowledge and beliefs on smoking as serious risk factor in cancer of urinary tract and lack of education and professional training in smoking cessation. This paper discusses what can be done to involve urologists in smoking cessation counselling and proposes a new 5As-based scheme of brief intervention tailored to the needs of BCa cancer patients. Conclusions Urologists who treat patients with disease of urinary or urogenital tract, patients at the risk of cancer or diagnosed cancer patients may play an essential role in helping their patients cease smoking. Their cessation efforts should be focused on cancer patient-tailored brief intervention and collaboration with specialized smoking cessation resources.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".