Smoking Cessation after Cancer Diagnosis and Enhanced Therapy Response: Mechanisms and Significance
Bibliographic record
Abstract
The adverse effects of smoking on human health have been recognized for several decades, especially in the context of cancer. The ability of tobacco smoke components, including tobacco-specific carcinogens and additive compounds such as nicotine, to initiate or promote tumor growth have been described in hundreds of studies. These investigations have revealed the tumor-promoting activities of nicotine and other tobacco smoke components and have also recognized the ability of these agents to suppress the efficacy of cancer therapy; it is now clear that smoking can reduce the efficacy of most of the widely used therapeutic modalities, including immunotherapy, radiation therapy, and chemotherapy. Several studies examined if continued smoking after cancer diagnosis affected therapy response; it was found that while never smokers or non-smokers had the best response to therapy, those who quit smoking at the time of diagnosis had higher overall survival and reduced side-effects than those who continued to smoke. These studies also revealed the multiple mechanisms via which smoking enhances the growth and survival of tumors while suppressing therapy-induced cell death. In conclusion, smoking cessation during the course of cancer therapy markedly increases the chances of survival and the quality of life.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".