Optimizing the management of patients with small renal masses in a Canadian context: A Markov decision-analysis model
Bibliographic record
Abstract
INTRODUCTION: The management of patients with a small renal mass (SRM) varies significantly. The objective of this study was to determine which initial management strategy resulted in the greatest quality-adjusted life months (QALM) for an index patient with a SRM. METHODS: A Markov decision analysis was used to determine the effect of 1) treating patients with a partial nephrectomy (PN); 2) active surveillance (AS); and 3) renal mass biopsy on QALM over a 10-year horizon. All relevant health states were modelled. Biopsy sensitivity and specificity were modelled assuming an 80% prevalence of cancer using procedural pathology as the gold standard. Health state utilities were obtained from the Tufts Medical Centre Cost-Effective Analysis Registry. Deterministic sensitivity analyses were used to test key assumptions. RESULTS: Over a 10-year time horizon for a 70-year-old male with a 2 cm SRM, the biopsy strategy resulted in 38.07 QALM, whereas treating all patients with PN resulted in 37.69 QALM and AS in 36.25 QALM. The model was most sensitive to the probability that a patient would remain alive at baseline. Biopsy was the preferred strategy when sensitivity was greater than 77%. As the underlying probability of cancer increased, the threshold of renal mass biopsy sensitivity to still favor biopsy increased. CONCLUSIONS: Renal mass biopsy is the preferred initial management strategy for an index patient with a SRM to optimize QALM. When the probability of cancer is high, centers should aim for a sensitivity of at least 77% in order to consider a biopsy as the first strategy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".