Management of Small Renal Masses: American Society of Clinical Oncology Clinical Practice Guideline
Bibliographic record
Abstract
Purpose To provide recommendations for the management options for patients with small renal masses (SRMs). Methods By using a literature search and prospectively defined study selection, we sought systematic reviews, meta-analyses, randomized clinical trials, prospective comparative observational studies, and retrospective studies published from 2000 through 2015. Outcomes included recurrence-free survival, disease-specific survival, and overall survival. Results Eighty-three studies, including 20 systematic reviews and 63 primary studies, met the eligibility criteria and form the evidentiary basis for the guideline recommendations. Recommendations On the basis of tumor-specific findings and competing risks of mortality, all patients with an SRM should be considered for a biopsy when the results may alter management. Active surveillance should be an initial management option for patients who have significant comorbidities and limited life expectancy. Partial nephrectomy (PN) for SRMs is the standard treatment that should be offered to all patients for whom an intervention is indicated and who possess a tumor that is amenable to this approach. Percutaneous thermal ablation should be considered an option if complete ablation can reliably be achieved. Radical nephrectomy for SRMs should only be reserved for patients who possess a tumor of significant complexity that is not amenable to PN or for whom PN may result in unacceptable morbidity even when performed at centers with expertise. Referral to a nephrologist should be considered if chronic kidney disease (estimated glomerular filtration rate < 45 mL/min/1.73 m 2 ) or progressive chronic kidney disease occurs after treatment, especially if associated with proteinuria.
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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".