The impact of active smoking on survival outcome in metastatic renal cell carcinoma patients treated with targeted therapy.
Bibliographic record
Abstract
552 Background: Smoking increases the risk of developing renal cell carcinoma (RCC). The prognosis of active smokers compared with non-smokers with metastatic RCC (mRCC) has not been well characterized. Methods: Smoking data from 1,842 patients with mRCC treated with targeted therapy were collected through the International mRCC Database Consortium (IMDC) from 8 Cancer Centers. Patients were categorized as current, former and non-smokers at the time of starting targeted therapy, and analyzed for differences in IMDC risk criteria, response rate (RR), progression- free-, (PFS) and overall survival (OS). Results: Overall, 292 (15.9%), 755 (41.0%), and 795 (43.1%) were current, former, and non-smokers. There were no differences in sarcomatoid features, number of metastatic sites or non-clear cell histology in either former or current smokers when compared with non-smokers. Likewise, former smokers had statistically similar IMDC risk groups compared to non-smokers. However, current smokers were more likely to have hypercalcemia (p=0.004), neutrophilia (p=0.038), thrombophilia (p=0.002), and more patients had higher numbers of IMDC risk features (p=0.014) when compared with non-smokers. The RR at first-line targeted therapy of former (p=0.89) and current smokers (p=0.13) were similar to non-smokers. No differences in PFS were noted: 7.7 vs. 7.6 vs. 6.2 months (mo) in non-, former (p=0.92), and current smokers (p=0.66). Interestingly, while former- and non-smokers had comparable OS times (23.7 vs. 23.1 mo; p=0.71), current smokers had significantly shorter OS (15.8 mo; p=0.001) than non-smokers. Current but not former smoking status was an independent poor prognosis factor (HR=1.28; p=0.006) when adjusted for the IMDC risk criteria. Moreover, each pack year increased the risk of death 1% (HR=1.01; p=0.039). Conclusions: Active smoking is associated with more advanced IMDC risk criteria and diminished OS in mRCC patients treated with targeted therapy agents. On the other hand, patients who quit smoking returned to a similar risk of death compared to patients who never smoked. Smoking cessation should be a counselling priority among mRCC patients receiving targeted agents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".