Frailty predicts outcome of partial nephrectomy and guides treatment decision towards active surveillance and tumor ablation
Bibliographic record
Abstract
PURPOSE: To examine frailty and comorbidity as predictors of outcome of nephron sparing surgery (NSS) and as decision tools for identifying candidates for active surveillance (AS) or tumor ablation (TA). METHODS: Frailty and comorbidity were assessed using the modified frailty index of the Canadian Study of Health and Aging (11-CSHA) and the age-adjusted Charlson-Comorbidity Index (aaCCI) as well as albumin and the radiological skeletal-muscle-index (SMI) in a cohort of n = 447 patients with localized renal masses. Renal tumor anatomy was classified according to the RENAL nephrometry system. Regression analyses were performed to assess predictors of surgical outcome of patients undergoing NSS as well as to identify possible influencing factors of patients undergoing alternative therapies (AS/TA). RESULTS: Overall 409 patient underwent NSS while 38 received AS or TA. Patients undergoing TA/AS were more likely to be frail or comorbid compared to patients undergoing NSS (aaCCI: p < 0.001, 11-CSHA: p < 0.001). Gender and tumor complexity did not vary between patients of different treatment approach. 11-CSHA and aaCCI were identified as independent predictors of major postoperative complications (11-CSHA ≥ 0.27: OR = 3.6, p = 0.001) and hospital re-admission (aaCCI ≥ 6: OR = 4.93, p = 0.003) in the NSS cohort. No impact was found for albumin levels and SMI. An aaCCI > 6 and/or 11-CSHA ≥ 0.27 (OR = 9.19, p < 0.001), a solitary kidney (OR = 5.43, p = 0.005) and hypoalbuminemia (OR = 4.6, p = 0.009), but not tumor complexity, were decisive factors to undergo AS or TA rather than NSS. CONCLUSION: In patients with localized renal masses, frailty and comorbidity indices can be useful to predict surgical outcome and support decision-making towards AS or TA.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".