Percutaneous radiofrequency ablation of small renal tumors using CT-guidance: a review and its current role.
Bibliographic record
Abstract
PURPOSE: To provide key evidence-based strategies to improve outcomes of radiofrequency ablation and limit recurrences of small renal tumors. MATERIALS AND METHODS: The literature was searched via OvidSP MEDLINE from 1997 to current using MeSH terms. All levels of evidence and types of reports were reviewed. RESULTS: We comprehensively reviewed technical issues, mechanisms, imaging criteria, ablative success, enhancement within one month, contraindications, oncological efficacy, morbidity rates, and follow-up strategies. CONCLUSION: The technique is safe and effective. Tumors < 2.5 cm are statistically most likely to remain disease-free. Anterior tumors are contraindicated. Strict follow-up is needed to detect failures, most of which occur within 3 months and can be easily salvaged with repeat radiofrequency ablation. Homogeneous enhancement within 1 month is not necessarily a failure, and tends to disappear after 4 to 6 weeks. Multi-disciplinary meetings must occur to discuss each case prior to treatment.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".