Cryotherapy and radiofrequency ablation: pathophysiologic basis and laboratory studies
Bibliographic record
Abstract
PURPOSE OF REVIEW: There is increasing interest in minimally invasive alternatives to surgery, especially as the natural history of small renal masses appears in the majority to be that of very slow growth. Cryoablation and radiofrequency ablation are two energy-based therapies that can be applied in a minimally invasive manner. We will review the recent clinical and laboratory studies that have formed the scientific foundation of the current clinical protocols and how these protocols may change in light of recent observations. RECENT FINDINGS: Although there is literature supporting enhanced cell death with the use of a passive thaw in cryoablation, recent data suggest that the use of an active thaw is no different. The active thaw process will effectively cryoablate renal tissue as well as significantly reduce overall operative time. There is lack of uniformity in the effectiveness of radiofrequency ablation for renal masses. It has been concluded that hematoxylin and eosin staining is inadequate for assessment of cell viability after radiofrequency ablation and thus, nicotinamide adenine dinucleotide staining should be included in the histological assessment of tissue. SUMMARY: Cryoablation is the most studied modality and its ability to both directly and indirectly damage cells is generally understood. Clinical experience will further refine knowledge about optimal freezing temperature and freeze-thaw cycles. The coagulation necrosis of radiofrequency ablation is an effective means of destroying cancerous tissue but targeting this energy has been difficult and treatment failures have occurred.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".