Kidney Cancer Research Network of Canada (KCRNC) consensus statement on the role of renal mass biopsy in the management of kidney cancer
Bibliographic record
Abstract
The pervasive use of diagnostic imaging has led to an increase in the incidental detection of small renal masses.1–4 The assessment and management of a patient with a renal mass should vary based on mass characteristics and on the individual patient’s health and personal preferences. Renal mass biopsy is a diagnostic test used to obtain tissue from a suspicious mass in the kidney. Several patient factors and mass characteristics should be considered to determine when a biopsy is a useful test for a patient. Recently, there have been a number of published series on renal mass biopsy that discuss which patient populations benefit from this diagnostic test.5–7 The objectives of this consensus statement are: 1) to review and synthesize the evidence on renal mass biopsy; and 2) to highlight important concepts and provide guidance regarding the role of renal mass biopsy. The statements contained in this report were based on the best available evidence and developed by expert consensus. It is expected that these statements will be used to guide care in Canada and that some variability in practice will exist for individual patients and regional practice variation. The scientific literature available for this consensus statement was of low-to-moderate-quality. The evidence reported on renal mass biopsy is predominantly comprised of retrospective cohort series of patients managed at high-volume centers.5–7 Recently, a systematic review and meta-analysis of renal mass biopsy was published, which summarizes the best available evidence on the diagnostic ability and safety of this test.7
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 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 | 0.080 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".