Population‐based assessment of cancer‐specific mortality after local tumour ablation or observation for kidney cancer: a competing risks analysis
Bibliographic record
Abstract
OBJECTIVES: To examine, using competing risks regression, differences in cancer-specific mortality (CSM) that might distinguish between local tumour ablation (LTA) and observation (OBS) for patients with kidney cancer. PATIENTS AND METHODS: The study focused on 1 860 patients with cT1a kidney cancer treated with either LTA or OBS between 2000 and 2009 in the Surveillance Epidemiology and End Results-Medicare database. Propensity-score matching was used. The study outcome was CSM. Multivariable competing risks regression analyses, adjusting for other-cause mortality as well as patient (including comorbidities) and tumour characteristics, were fitted. RESULTS: Overall, fewer patients underwent LTA than OBS (30 vs 70%; n = 553 vs n = 1 307). Compared with patients in the OBS group, those in the LTA group were younger (median age 77 vs 78 years; P < 0.001), more likely to be white (84 vs 78%; P = 0.005), more frequently married (59 vs 52%; P = 0.02) and more frequently of high socio-economic status (54 vs 45%; P = 0.001). After propensity-score matching, 553 patients who underwent LTA and 553 who underwent OBS remained for subsequent analyses. The mean standardized differences of patient characteristics between the two groups were <10%, indicating a high degree of similarity. After LTA or OBS, the 5-year CSM estimates from Poisson regression-derived smoothed plots were 3.5 and 9.1%, respectively. In multivariable competing risks regression analyses, LTA use was found to have a protective effect on CSM (hazard ratio 0.47 [95% confidence interval 0.25-0.89]; P = 0.02). CONCLUSIONS: After adjustment for comorbidity and tumour characteristics in elderly patients with kidney cancer, LTA was associated with a clinically and statistically significant protective effect on CSM, compared with OBS. This advantage of LTA deserves consideration when obtaining informed consent.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".