Identifying the use and barriers to the adoption of renal tumour biopsy in the management of small renal masses
Bibliographic record
Abstract
INTRODUCTION: Renal tumour biopsies (RTBs) can provide the histology of small renal masses (SRMs) prior to treatment decision-making. However, many urologists are reluctant to use RTB as a standard of care. This study characterizes the current use of RTB in the management of SRMs and identifies barriers to a more widespread adoption. METHODS: A web-based survey was sent to members of the Canadian and Quebec Urological Associations who had registered email address (n=767) in June 2016. The survey examined physicians' practice patterns, RTB use, and potential barriers to RTB. Chi-squared tests were used to assess for differences between respondents. RESULTS: The response rate was 29% (n=223), of which 188 respondents were eligible. A minority of respondents (12%) perform RTB in >75% of cases, while 53% never perform or perform RTB in <25% of cases. Respondents with urological oncology fellowship training were more likely to request a biopsy than their colleagues without such training. The most frequent management-related reason for not using routine RTB was a belief that biopsy won't alter management, while the most frequent pathology-related reason was the risk of obtaining a false-negative or a non-diagnostic biopsy. CONCLUSIONS: Adoption of RTBs remains low in Canada. Concerns about the accuracy of RTB and its ability to change clinical practice are the largest barriers to adoption. A knowledge translation strategy is needed to address these concerns. Future studies are also required in order to define where RTB is most valuable and how to best to implement it.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".