Comparison of RCC surveillance guidelines: Competing tradeoffs.
Bibliographic record
Abstract
6594 Background: Renal cell carcinoma (RCC) is now detected at earlier stages than in the past due to increased use of abdominal imaging for unrelated medical conditions. Three organizations offer differing guidelines for surveillance imaging strategies following partial nephrectomy (PN). We hypothesized that published surveillance patterns would have previously unrecognized sizable differences in intensity, cost, and radiation exposure and real world experience would not adhere to guidelines. Methods: Cost ($2,012) and radiation exposure in millisieverts (mSv) were compared between the following surveillance guidelines:American Urological Association (AUA), Canadian Urology Association (CUA), and the European Association of Urology (EAU). These were stratified into low-risk and high-risk based on tumor characteristics and compared to patients undergoing PN for RCC at the from 2009-2010. The intensity, cost, and estimated radiation exposure were calculated for each subject. We used the Welch’s t-test to test for significant differences in cost and radiation exposure between the low-risk and high-risk groups. Results: The median cost of low risk surveillance from least expensive to most expensive is as follows: CUA ($481), EAU ($928) and AUA ($1,320). For high risk surveillance, least expensive was CUA ($543), EAU ($1,447) and AUA ($2,621). Radiation exposure was least for the CUA guidelines (low and high risk was 16.2 and 16.4 mSv ) and highest for the AUA guidelines at ( low and high risk was 48mSv and 92). The EUA guideline exposures were 23 mSv and 46mSv for low and high risk respectively. For comparison with a real world subset, 43/63 patients undergoing PN had complete records. We identified wide variability in intensity, frequency, and modality of surveillance imaging. These were not correlated to risk category or guidelines. Conclusions: Published surveillance strategies for RCC following PN differ greatly in terms of cost and radiation dose. Practicing clinicians do not seem to stratify patients by tumor-risk category as recommended by the published guidelines. It is important for clinicians to adopt standardized surveillance strategies that limit unnecessary cost and radiation exposure.
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 | 0.046 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".