Predicting cancer-control outcomes in patients with renal cell carcinoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE OF REVIEW: An increasing number of models are becoming available for patients with either suspected or established renal cell carcinoma (RCC) of various stages. In this review, we propose a systematic approach to the assessment of the quantity of the existing predictive and prognostic models. RECENT FINDINGS: Only one model was designed to distinguish between malignant or benign histology prior to nephrectomy and another tool attempts to discriminate between low-grade and high-grade histology. Four tools predict the natural history of RCC using preoperative tumor characteristics. Postnephrectomy recurrence can be predicted with four tools. Finally, mortality predictions can be quantified with 21 predictive tools. Although several of these tools are validated, formal tests were performed in surprisingly few such models. SUMMARY: Multiple models can be applied to nephrectomy candidates, to patients treated with nephrectomy, or to individuals with metastatic RCC regardless of nephrectomy status. For newly diagnosed and untreated patients, these tools can guide the clinician with respect to treatment selection. For patients treated with nephrectomy, they can assess the risk of recurrence and/or mortality and can guide the type and frequency of follow-up considerations. Finally, for patients with metastatic RCC, the models can provide the best estimate of remaining life expectancy. Unfortunately, virtually no data are available to model the prognosis of patients subjected to surveillance or nonextirpative treatment models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it