Identification of tumor size as the only factor associated with non-diagnostic biopsies in patients with small renal masses
Bibliographic record
Abstract
INTRODUCTION: As greater numbers of small renal masses (SRMs) are discovered incidentally, renal tumor biopsy (RTB) is an increasingly recognized step for the management of these lesions, ideally for the prevention of surgical overtreatment for benign disease. While the diagnosis can often be obtained preoperatively by RTB, indeterminate results create greater difficulty for patients and clinicians. This study examines a series of RTBs, identifying the portion of these that were able to yield a diagnosis, and correlates patient factors, including RENAL and PADUA scoring, with the outcome of a non-diagnostic result. METHODS: Patients were identified as having undergone RTB at the Princess Margaret Cancer Centre in Ontario, Canada, between January 2000 and December 2009. Data was compiled from these 423 patients and analyzed using CART methodology to determine the level of association between various patient and tumor factors and the outcome of a non-diagnostic biopsy. Tumor size was further used to develop a classification tree to describe the prediction of a non-diagnostic biopsy. RESULTS: Of these 423 patients undergoing RTB, 66 (16%) resulted in a non-diagnostic biopsy. The only patient or tumor factor that was found to be associated with a non-diagnostic outcome was mass size, where small masses (<1.28 cm diameter) were found to have a 38% chance of being non-diagnostic, compared with a 13% chance in those tumors >1.28 cm diameter (86% accuracy, 95% confidence interval [CI] 0.82-0.89). CONCLUSIONS: When evaluating SRMs for diagnostic workup, mass size is the only tumor or patient characteristic associated with a non-diagnostic RTB.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".