A novel clinical decision aid to support personalized treatment selection for patients with CT1 renal cortical masses: Results from a multi-institutional competing risks analysis including performance status and comorbidity.
Bibliographic record
Abstract
610 Background: Personalized treatment for clinical T1 renal cortical masses (RCMs) should account for competing risks related to tumor and patient characteristics. Using a contemporary multi-institutional cohort, we developed treatment-specific prediction models for cancer-specific mortality (CSM), other-cause mortality (OCM), and 90-day complication rates for patients managed with surgery, thermal ablation (TA), and active surveillance (AS). Methods: Preoperative clinical and radiological features were collected for eligible patients aged 18-91 years treated at four academic centers from 2000-2016. Prediction models used competing risks regressions for CSM and OCM and logistic regressions for 90-day Clavien >3 complications, adjusting for tumor size as well as patient age, sex, ECOG performance status (PS), and Charlson comorbidity index (CCI). Predictions accounted for missing data using multiple imputation. Results: After excluding 25 patients with no follow-up, the cohort included 4995 patients treated with radical nephrectomy (RN, n=1270), partial nephrectomy (PN, n=2842), thermal ablation (n=479), or active surveillance (n=404). Median follow-up was 5.1 years (IQR 2.5-8.5). Predictions from the fitted model are shown in an online calculator ( https://rgulati.shinyapps.io/rcc-risk-calculator ). To illustrate the use of this calculator for a specific patient, a 70-year-old female with a 5.5 cm RCM, PS of 2, and CCI of 3 has a predicted 5-year CSM of 4-7% across treatments, 5-year OCM of 34-49%, and 90-day risk of Clavien ≥3 complications of 4%, 10%, and 6% for RN, PN, and TA respectively. Conclusions: Personalized treatment selection for cT1 RCM is challenging. We present a competing risk calculator that incorporates pretreatment features to quantify competing causes of mortality and treatment-associated complications. Pending validation, this tool may be used in clinical practice to provide patients with estimated individualized treatment-specific probabilities of competing causes of death and complication risks to facilitate shared decision-making.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".