The current management of small renal masses
Bibliographic record
Abstract
PURPOSE OF REVIEW: Small renal masses are increasingly being discovered incidentally on routine abdominal imaging performed for other purposes. The natural history of small renal masses as well as the therapeutic approach for such lesions is an evolving paradigm. In this review, we assess the current literature regarding this controversial topic. RECENT FINDINGS: Nephron-sparing surgery can be performed with minimal morbidity and mortality. Ablative techniques, such as cryoablation and radio frequency ablation, have shown both safety and reliability in series reporting short-term oncologic follow-up. Preliminary studies seem to favor cryoablation when compared with radio frequency ablation. Observational series have shown that very few of these selected patients present disease progression while on active surveillance. Finally, recent studies have shown that renal biopsy is both well tolerated and accurate. SUMMARY: The management of small renal masses remains a challenging issue. Although nephron-sparing surgery is the standard of care for these masses, ablative techniques and observation represent adequate alternatives, especially for poor surgical candidates. Renal biopsy is increasingly proving to be of value in the management of small renal masses.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".