A review of the effects of tobacco smoking on the treatment of prostate cancer
Bibliographic record
Abstract
Abstract Background: Prostate cancer is the most commonly diagnosed malignancy and the third leading cause of death among Canadian men. The standard treatment modalities for prostate cancer include prostatectomy, radiation therapy, hormonal therapy and chemotherapy or any combination depending on the stage of the tumour. However, several studies have reported that tobacco smoking at the time of diagnosis and during treatment can potentially impact treatment efficacy, outcome and patients quality of life after treatment. Materials and methods: This narrative literature review elucidates the impacts of tobacco smoking on prostate cancer progression, treatment efficacy, including its effects on prostatectomy, radiation therapy and chemotherapy, risk of cancer recurrence and mortality and quality of life after treatment. Furthermore, we discuss the importance of integrating a smoking cessation programme into the treatment regimen for prostate cancer patients in order to yield more favourable treatment outcomes, reduce risk of recurrence and mortality and increase the quality of life after treatment for prostate cancer patients. Conclusions: Smoking cessation is one of the most important interventions to prevent cancer and it is also essential after the diagnosis of prostate cancer to improve clinical outcomes. All prostate cancer patients should be advised to quit tobacco use since it can potentially improve treatment response rates and survival, as well as reduce the risk of developing treatment complications and potentially improve the quality of life after treatment. There are several benefits to smoking cessation and it should become an important component of the cancer care continuum in all oncology programmes, starting from prevention of cancer through diagnosis, treatment, survivorship and palliative care. Evidence-based smoking cessation intervention should be sustainably integrated into any comprehensive cancer programme, and the information should be targeted to the specific benefits of cessation in cancer patients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".