Delay in Nephrectomy and Cancer Control Outcomes in Elderly Patients with Small Renal Masses
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To examine the impact of nephrectomy delay on the survival of patients with small renal masses. METHODS: Relying on the Surveillance, Epidemiology, and End Results Medicare-linked database, 6,237 patients with pT1a renal cell carcinoma who underwent radical or partial nephrectomy were identified (1988-2005). Nephrectomy delay was dichotomized as ≤3 vs. >3 months. Uni- and multivariate Cox regression analyses tested the effect of delayed nephrectomy on cancer-specific mortality (CSM). In sub-analyses, various other time from diagnosis to nephrectomy cut-offs were modelled: (a) ≤1 vs. >1 month, (b) ≤2 vs. >2 months, (c) ≤4 vs. >4 months, (d) ≤6 vs. >6 months, (e) ≤12 vs. >12 months or (f) continuously coded. RESULTS: In univariate analyses, nephrectomy delay >3 months was associated with a higher risk of CSM (hazard ratio [HR]: 2.07; 95% confidence interval [CI]: 1.58-2.72; p < 0.001). However, after multivariate adjustment, a nephrectomy delay >3 months was not significantly associated with a higher risk of CSM (HR: 1.33; 95% CI: 0.96-1.86; p = 0.09). The lack of a relationship between nephrectomy delay and CSM after multivariate adjustment persisted even in various sub-analyses of other categorizations for nephrectomy delay. CONCLUSIONS: In the case of eventual nephrectomy delay among patients with small renal masses, CSM is unaffected.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it