The benefits of quitting smoking for people with cancer
Bibliographic record
Abstract
Introduction Smoking has been linked to cancer occurrence and survival rates for a long time. However, there is little research into the impacts of smoking on oncological treatments. The aim of the study is to show the impact that smoking can have on the effects of cancer therapy. Methods Epidemiological situation analysis of the malignant diseases based on the data of incidence and mortality and the impact of smoking on the results of treatment. Results At the time of establishing the diagnosis, the incidence of smoking varies from 10 to 95 per cent ( 60% for lung cancer). The previous research indicates that continuing smoking after the establishment of a diagnosis can impact the treatment outcomes and is related to worse prognosis and survival rate. Active smoking can be connected to lower survival rates with advanced non-small cell lung cancer, limited small cell lung cancer, bladder cancer, and upper tract urothelial cancer. The smokers suffering from prostate cancer have a higher risk of death outcomes and worse prognoses after the treatment. The research has shown that there is a link between smoking and slower wound healing in operated patients and that it can also intensify the side-effects of chemotherapy and prolong the responses to radiation. Conclusions It is essential to increase knowledge about the health benefits of quitting smoking in order to advance the results of oncological treatments. Implemented smoking cessation programs need to be adapted to oncology patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".