Thermal Therapy, Part III: Ablation Techniques
Bibliographic record
Abstract
Ablative treatments are gaining increasing attention as an alternative to standard surgical therapies, especially for patients with contraindication or those who refuse open surgery. Thermal ablation is used in clinical applications mainly for treating heart arrhythmias, benign prostate hyperplasia, and nonoperable liver tumors; there is also increasing application to other organ sites, including the kidney, lung, and brain. Potential benefits of thermal ablation include reduced morbidity and mortality in comparison with standard surgical resection and the ability to treat nonsurgical patients. The purpose of this review is to outline and discuss the engineering principles and biological responses by which thermal ablation techniques can provide elevation of temperature in organs within the human body. Because of the individual problems associated with each type of treatment, a wide range of ablation techniques have evolved including cryoablation as well as ultrasound, radiofrequency (RF), microwave, and laser ablation. Aspects of each ablation technique, including mechanisms of action, equipment required, selection of eligible patients, treatment techniques, and patient outcomes are presented, along with a discussion of limitations of the techniques and future research directions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".