Should Breast Cancer Patients Be The Focus Of Anti-Smoking Campaigns In Transitional Countries?
Bibliographic record
Abstract
INTRODUCTIONStudies have shown that breast cancer patients who smoke during cancertreatment, along with a higher symptom burden, exhibit a dramatically higher risk of distant metastasis (up to HR 4.19) and death (up to HR 3.52)1. The majority of studies report that only 8-14% breast cancer survivors smoke, although recent research shows lowsmokingcessation in breast cancer patients in Western population after cancer diagnosis. Currently, although around 30% of females in Croatia are active smokers, no analysis regardingsmokingcessation has been made.METHODSThe study was cross-sectional research in two clinical hospital centers in Croatia and involved 168 breast cancer patients undergoing activetreatment.RESULTSBreast cancer patients are relatively young (57.6u00b111.31 years) and rarely metastatic (22%). Only around a quarter of breast cancer patients smoked at the time of cancer diagnosis. However, 96% of breast cancer patients continue to smoke after a cancer diagnosis, although the percentage of patients who smoke over ten cigarettes a day dropped from 70% to 36%. However, these numbers are higher compared to other patients and are much higher than previously reported for the Western population.CONCLUSIONAlthough breast cancer patients are expected to exhibit relatively long survival compared to othercancers, they are one of the patient groups least likely to engage insmokingcessation during thetreatment, possibly due to low perceived risk. With new research emphasizing the dangers ofsmokingduring cancertreatment, breast cancer patients should be one of the focus groups, especially intransitionalcountrieswith a high number of female smokers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".