The impact of smoking on adjuvant breast cancer radiation treatment: A systematic review
Bibliographic record
Abstract
BACKGROUND: The influence of cigarette smoking on cancer risk has been well-studied. Similarly, exposure to ionizing radiation from radiotherapy (RT) can produce detrimental effects on an individual's health. In patients administered RT, there has been an observed relationship in other primary carcinomas. The purpose of this systematic review was to summarize the influence of cigarette smoking on outcomes post adjuvant RT in breast cancer patients. METHODS: OVID Medline, Cochrane and Embase were searched and 1893 articles were identified. A total of 71 articles were included in the review. Study type, published year and sample size, age, systemic therapies, RT techniques and treatment side effects were collected if available. RESULTS: The review found 198 different outcomes which fell into 7 categories and similar outcomes were recorded. 40% of skin reaction outcomes, 50% of cardiovascular outcomes, 71% of reconstruction outcomes, 29% of pulmonary function outcomes, 33% of mortality outcomes and 42% of secondary recurrence outcomes reported significant differences between smokers and non-smokers. None of the articles reported non-smokers to have a higher risk than smokers. CONCLUSION: Cigarette smoking can pose a higher risk of post-treatment complications that can influence an individual's quality of life, survival rate and/or recurrence risk. This review further assessed the impact of smoking on various patient outcomes and side-effects in the adjuvant breast RT setting. The information provided in this review suggest that smoking cessation programs would help educate patients to understand their risks of being a current or former smoker when undergoing RT.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".