Estimating Prognosis of Patients with Kidney Cancer
Bibliographic record
Abstract
Kidney Cancer has numerous subtypes with Clear Cell Renal Cell Carcinoma (ccRCC) being the most common. Pre-existing prognostic models have not been validated in Canadian patients for recurrence free survival (RFS) and other outcomes. We conducted four studies: 1) externally validated pre-existing RCC prognostic models; 2) assessed the impact of baseline hazard function miscalibration on model assessment; 3) created new models and risk groups for RFS in non-metastatic ccRCC patients; 4) compared new risk groups to existing Canadian guidelines and created new imaging schedules. Pre-existing model performance varied considerably with some models performing well. The effect of baseline hazard function miscalibration varied across distribution shapes but the calibration slope was useful in relatively ranking prognostic model performance. The CKCis prognostic model and risk groups performed better than the existing CUA risk groups. Based on CKCis risk groups fewer scans are recommended in low-risk patients and more scans are recommended in higher risk patients. External validation of the CKCis model is required to assess clinical utility in different populations.
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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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".