Conditional survival after nephrectomy for renal cell carcinoma ( <scp>RCC</scp> ): changes in future survival probability over time
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of length of survival on future survival probability, otherwise known as the effect of conditional survival (CS), after nephrectomy (NT) in patients diagnosed with renal cell carcinoma (RCC). PATIENTS AND METHODS: Overall, 42,090 patients with RCC who underwent NT were abstracted from the Surveillance, Epidemiology, and End Results database (1988-2008). Based on cumulative survival estimates, CS rates were derived according to patient and disease characteristics. Separate multivariable Cox regression analyses were performed for the prediction of cancer-specific mortality (CSM), according to 1-, 2-, 3-, 4- and 5-year survival postoperatively. RESULTS: Immediately after surgery, the 5-year cancer-specific survival rate was 83.5%. Amongst patients who survived ≥1, ≥2, ≥3, ≥4, and ≥5 years after NT, the probability rates for surviving an additional 5 years were 87.0, 89.6, 90.9, 92.0 and 92.3%, respectively. Provided that patients survived 1 and 2 years after NT, the probability of being CSM-free for another 5 years increased by +4.1 and 4.3% for stage III and +12.9 and 10.3% for stage IV disease, respectively. Similar observations were recorded for patient age, grade, nodal stage and tumour size, and were confirmed upon multivariable analyses. CONCLUSION: Survival probabilities vary according to length of survival after NT. Specifically, even amongst patients with more advanced disease at surgery, a more favourable prognosis can be achieved after surviving for 1-2 years.
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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 | 0.006 |
| 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 | 0.000 | 0.001 |
| 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".