Impact of smoking status and cumulative exposure on intravesical recurrence of upper tract urothelial carcinoma after radical nephroureterectomy
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of cigarette smoking status, cumulative smoking exposure, and time from cessation on intravesical recurrence (IVR) outcomes in patients treated with radical nephroureterectomy (RNU) for upper tract urothelial carcinoma (UTUC). PATIENTS AND METHODS: In all, 519 patients underwent RNU at five institutions. Smoking history included smoking status, quantity of cigarettes smoked per day (cpd), duration, and time from cessation. The cumulative smoking exposure was categorised as light-short-term (≤19 cpd and ≤19.9 years), moderate (all combinations except light-short-term and heavy-long-term), and heavy-long-term (≥20 cpd and ≥20 years). Univariable/multivariable cox regression analyses assessed the effects of smoking on IVR. RESULTS: In all, 190 patients (36%) never smoked; 205 (40%) and 125 (24%) were former and current smokers, respectively. Among smokers, 42 (8%), 185 (36%), and 102 (20%) patients were light-short-term, moderate, and heavy-long-term smokers, respectively. Within a median follow-up of 37 months, 152 patients (29%) had IVR. Actuarial IVR-free-survival estimates (standard error) at 2, 5, and 10 years were 72 (2)%, 58 (3)%, and 51 (4)%, respectively. In multivariable analyses, current smoking status, smoking intensity (≥20 cpd), smoking duration (≥20 years), and heavy-long-term smoking were associated with higher risk of IVR (all P ≤ 0.01). Patients who quit smoking ≥10 years before RNU had better IVR outcomes than current smokers and those patients who quit smoking <10 years before RNU. CONCLUSIONS: Cigarette smoking is significantly associated with IVR in patients treated with RNU for UTUC. Current and heavy-long-term smokers have the highest risk of IVR. Smoking cessation for >10 years before RNU seems to mitigate these detrimental effects.
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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 | 0.001 |
| 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) | 0.001 | 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".