An analysis of patients with <scp>T</scp> 2 renal cell carcinoma ( <scp>RCC</scp> ) according to tumour size: a population‐based analysis
Bibliographic record
Abstract
OBJECTIVE: To examine the discriminant properties of the most contemporary version of the Tumour-Node-Metastasis (TNM) staging for renal cell carcinoma (RCC) sub-classification of T2 lesions according to a threshold size of 10 cm. Other thresholds were also assessed. PATIENTS AND METHODS: Between 1988 and 2006, within the Surveillance, Epidemiology, and End Results database, patients with T2 N0-2 M0-1 RCC treated with a nephrectomy were abstracted. Tumour size was evaluated according to several thresholds: ≥8, ≥9, ≥10, ≥11, and ≥12 cm. Kaplan-Meier and life tables for cancer-specific mortality (CSM) were computed. Several Cox regression modes were fitted for prediction of CSM, using different thresholds. The predictive accuracy of various thresholds was compared using the area under the curve and methods of calibration. RESULTS: In all, 4963 patients were identified. Kaplan-Meier analyses showed statistically significant CSM-free survival differences between all examined thresholds. In multivariable Cox-regression models, all tested tumour size thresholds emerged as independent predictors of CSM. Of all thresholds, the values of 9 (0.55) and 11 cm (0.55) achieved the highest discrimination in univariable analysis, followed by 10 (0.539), 12 (0.539), and 8 cm (0.531). When the thresholds were combined with all other variables, the 11 cm (0.688) achieved the highest discrimination. CONCLUSION: The discriminant properties of all examined thresholds showed very similar discriminant properties, which brings into questioning whether a dichotomization of pT2 tumours is really necessary.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".