Safety, reliability and accuracy of small renal tumour biopsies: results from a multi‐institution registry
Bibliographic record
Abstract
OBJECTIVE: To validate, in a multi-institution review, the safety, accuracy and reliability of renal tumour biopsy (RTB) and its role in decreasing unnecessary treatment. MATERIALS AND METHODS: We conducted a multi-institution retrospective study of patients who underwent RTB to characterize a small renal mass (SRM) between 2011 and May 2015. Patients were identified using the prospectively maintained Canadian Kidney Cancer information system. Diagnostic and concordance rates were presented using proportions, whereas factors associated with a diagnostic RTB were identified using a logistic regression model. RESULTS: Of the 373 biopsied SRMs, the initial biopsy was diagnostic in 87% of cases. Of the 47 non-diagnostic biopsies, 15 had a repeat biopsy of which, 80% were diagnostic. When both were combined, therefore, a diagnosis was obtained in 91% of SRMs. Of these, 18% were benign. Size was the only factor found to be associated with achieving a diagnostic biopsy. RTB histology and nuclear grade (high or low) were found to be highly concordant with surgical pathology (86 and 81%, respectively). Of the discordant tumours (n = 16), all were upgraded from low to high grade on surgical pathology. Adverse events were rare (<1% of cases). CONCLUSION: The present multi-institution study confirms that RTB of SRMs is safe, accurate and reliable across institutions, while decreasing unnecessary treatment. Given our findings, RTBs may be a helpful tool with which to triage SRMs and guide appropriate management.
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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 | 0.008 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".