Tumour location as a predictor of benign disease in the management of renal masses
Bibliographic record
Abstract
OBJECTIVE: To investigate the association between tumour location and the proportion of benign disease in renal masses presumed to be renal cell carcinoma (RCC) preoperatively. METHODS: This Institutional Review Board approved study includes 196 patients who underwent surgical treatment for renal masses <5 cm at our institution by a single surgeon between January 2002 and June 2009. Based on preoperative imaging, each mass was designated as central (touching or encroaching upon the renal collecting system and/or renal sinus) or peripheral. The association between tumour location and benign pathology was determined using univariate and multiple logistic regression, including tumour size and patient sex in the model. RESULTS: The proportion of histologically confirmed benign disease in this series was 11.2%. The proportion of benign disease by location was 5.9% and 19.5% for central and peripheral masses, respectively. The effect of location was found to have a significant prognostic value (p = 0.0273) with an adjusted odds ratio of 3.51 (95% CI = 1.38-19.62) for the odds of a benign diagnosis in peripheral compared to central tumours. Tumour size and patient sex were not significant predictors of benign pathology (p = 0.483 and 0.191, respectively). CONCLUSIONS: Peripherally located renal masses are more likely to be benign than centrally located renal masses. This information may be used when selecting strategies for the management of renal masses presumed to be RCC.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".