Extent of lymphadenectomy does not improve the survival of patients with renal cell carcinoma and nodal metastases: biases associated with the handling of missing data
Bibliographic record
Abstract
UNLABELLED: WHAT'S KNOWN ON THE SUBJECT? AND WHAT DOES THE STUDY ADD?: A recent population-based analysis suggested a potential survival benefit with respect to performing lymph node dissection at nephrectomy in node-positive patients with RCC. The findings of the present study failed to corroborate the association of a survival benefit with the performance of lymph node dissection at nephrectomy. OBJECTIVE: Previous studies showed no survival benefit with respect to performing lymph node dissection (LND) at nephrectomy, whereas a recent population-based analysis suggested otherwise, although the latter relied on imputation. To reconcile the findings of that study by critically evaluating the handling of missing data. PATIENTS AND METHODS: Study participants comprised patients diagnosed with non-metastatic renal cell carcinoma (RCC) of all stages who underwent LND at nephrectomy (n = 10 596). Multivariable Cox regression models were performed to predict cancer-specific mortality (CSM), where the primary variable of interest was the extent of LND. To examine differences in approaches with respect to handling missing data, separate analyses were performed: (i) imputed population; (ii) exclusion of patients with missing data; and (iii) inclusion of patients with missing data as a sub-category. RESULTS: Overall, 2916 (28%) patients had missing tumour grade. In multivariable analyses, our findings showed that increasing the extent of LND was associated with a significant protective effect on CSM in patients with pN1 after imputation (hazard ratio [HR], 0.82; P = 0.04). By contrast, the extent of LND was no longer significantly associated with a lower risk of CSM after excluding patients with a missing tumour grade (HR, 0.83; P = 0.1) or when including patients with missing tumour grade as a sub-category (HR, 0.82; P = 0.05). CONCLUSIONS: The findings of the present study failed to corroborate the association of a survival benefit with increasing extent of LND at nephrectomy. The different methodologies employed to account for missing data may introduce important biases. Such considerations are non-negligible with respect to the interpretation of results for investigators who rely on administrative cohorts.
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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 | 0.041 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".