Cigarette smoking cessation, duration of smoking abstinence, and head and neck squamous cell carcinoma prognosis
Bibliographic record
Abstract
BACKGROUND: Tobacco use is a major risk factor for developing head and neck squamous cell carcinoma (HNSCC). However, the prognostic associations with smoking cessation are limited. The authors assessed whether smoking cessation and increased duration of abstinence were associated with improved overall (OS) and HNSCC-specific survival. METHODS: Clinicodemographic and smoking data from patients with HNSCC at Princess Margaret Cancer Center (2006-2019) were prospectively collected. Multivariable Cox and Fine and Gray competing-risk models were used to assess the impact of smoking cessation and duration of abstinence on overall mortality and HNSCC-specific/noncancer mortality, respectively. RESULTS: Among 2482 patients who had HNSCC, former smokers (adjusted hazard ratio [aHR], 0.71; 95% CI, 0.58-0.87; p = .001; N = 841) had a reduced risk of overall mortality compared with current smokers (N = 931). Compared with current smokers, former smokers who quit >10 years before diagnosis (long-term abstinence; n = 615) had the most improved OS (aHR, 0.72; 95% CI, 0.56-0.93; p = .001). The 5-year actuarial rates of HNSCC-specific and noncancer deaths were 16.8% and 9.4%, respectively. Former smokers (aHR, 0.71; 95% CI, 0.54-0.95; p = .019) had reduced HNSCC-specific mortality compared with current smokers, but there was no difference in noncancer mortality. Abstinence for >10 years was associated with decreased HNSCC-specific death compared with current smoking (aHR, 0.64; 95% CI, 0.46-0.91; p = .012). Smoking cessation with a longer duration of quitting was significantly associated with reduced overall and HNSCC-specific mortality in patients who received primary radiation. CONCLUSIONS: Smoking cessation before the time of diagnosis reduced overall mortality and cancer-specific mortality among patients with HNSCC, but no difference was observed in noncancer mortality. Long-term abstinence (>10 pack-years) had a significant OS and HNSCC-specific survival benefit.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".