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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".