What is the best treatment strategy for incidentally detected small renal masses? A decision analysis
Bibliographic record
Abstract
OBJECTIVE: •To determine the optimal treatment for incidentally detected small renal masses between radical nephrectomy, partial nephrectomy, ablative therapy (AT) and active surveillance (AS) using a decision-analytic Markov model. SUBJECTS AND METHODS: •The reference case was an otherwise healthy 60-year-old patient. •Health utilities and probabilities for postoperative complications, progression to chronic renal insufficiency (CRI), local and systemic recurrence, disease-specific and all-cause mortality were derived from published literature. •Outcome measures included life expectancy and quality-adjusted life expectancy. •Extensive sensitivity analyses were performed, including probabilistic sensitivity analyses. RESULTS: •The mean life expectancy was 18.49 years for partial nephrectomy, 18.09 years for laparoscopic radical nephrectomy, 17.85 years for AT and 17.70 years for AS. •External validation of our model yielded similar cancer-specific survival rates to the published literature. •AS became preferred if age at presentation was >74 years, the probability of systemic recurrence on AS was <1.3%/year or when the hazard ratio of death with CRI was >1.63. •AT became preferred when the probability of systemic recurrence on AT was <1.2%/year, whereas laparoscopic radical nephrectomy was preferred when the risk of CRI with this treatment was <6.6%/year. CONCLUSIONS: •Based on current literature, our model emphasizes the importance of balance between disease control and preserving renal function on life expectancy, and justifies initial active intervention with partial nephrectomy in younger patients. •Our results are consistent with recent American Urological Association guidelines for the management of this disease. •However, data used in the model were mostly derived from retrospective data, and thus are subject to selection bias, particularly with respect to AS and AT.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".