Prognostic ability of simplified nuclear grading of renal cell carcinoma
Bibliographic record
Abstract
BACKGROUND: The Fuhrman grading system is an established predictor of survival in patients with renal cell carcinoma (RCC). The predictive accuracy of various Fuhrman grading schemes was tested with the intent of improving the prediction of RCC-specific survival (RCC-SS). METHODS: The analyses targeted 5453 patients from 14 institutions. Univariable, multivariable, and predictive accuracy analyses addressed RCC-SS. The statistical significance of the gain in predictive accuracy was quantified with the Mantel-Haenszel test. RESULTS: The median follow-up time was 4.5 years. In both univariable and multivariable analyses, Fuhrman grade achieved independent predictor status regardless of the coding scheme. When Fuhrman grade was not considered in multivariable analyses, the predictive accuracy was 83.8%. Addition of Fuhrman grade to the multivariable model resulted in predictive accuracy gains of 0.8% for all 3 grading schemes tested. CONCLUSION: Fuhrman grade must to be considered when RCC-SS is assessed. However, modified or conventional Fuhrman grading schemes perform equally well as the conventional grading system.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".