2023 UPDATE – Canadian Urological Association guideline: Management of cystic renal lesions
Bibliographic record
Abstract
The current guideline summarizes the state-of-the-art knowledge on the management of cystic renal lesions by updating the 2017 Canadian Urological Association (CUA) guideline on the topic.To do so, we updated our search strategy on June 18, 2022, and have identified 38 relevant articles, which led to a revision of the content of the original publication.The panel formulated several recommendations using the GRADE evidence to decision framework -a methodological improvement compared to the previous iteration.Three key recommendation changes were made compared to the previous iteration:1. Patients with a renal cyst should be classified as per the v2019 Bosniak classification.2. For Bosniak III or IV cyst measuring ≤2 cm, active surveillance is now suggested as the preferred strategy.3.For Bosniak III or IV cyst measuring 2-4 cm, active surveillance or surgery are suggested as equal options.The panel made these changes in an attempt to decrease the burden of care for patients, but also acknowledges the low-quality evidence supporting these changes.Consequently, we emphasize the need for shared decision-making.Patients opting for nonsurgical strategies should be made aware of the higher uncertainty surrounding the data supporting their treatment of choice.
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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".