Microwave-Assisted Chemical Ablation (MA-CA): A Novel Microwave-Assisted Tissue Ablation Procedure—Preliminary Assessment of Efficiency
Bibliographic record
Abstract
Microwave (MW) ablation is becoming a routine technology in the interventional radiology field. A new approach combining MW ablation and chemical ablation is developed in this paper. The rationale for the development of this Microwave-Assisted Chemical Ablation (MA-CA) technology was to improve the utility of thermal ablation as a minimally invasive treatment for cancer. The experimental conditions for ex vivo bovine liver samples were: A—100 W (120 s) with no addition of ethanol; B—100 W (30 s), wait (60 s) (no power), and 100 W (90 s) with no addition of ethanol; C—100 W (30 s), wait (60 s), 100 W (30 s), and 100 W (60 s) with the addition of 5 mL ethanol; and D—100 W (30 s), wait (60 s), 100 W (30 s), 0 W (30 s) with the addition of 2.5 mL ethanol, and 100 W (60 s) with the addition of 5 mL ethanol (12,000 Joules Total). The results showed that with the use of ethanol, the ablation zone was enlarged and revealed improved sphericity. This novel combination has greater advantages than either technology individually. The objective is to increase the precision and efficiency of MW ablation and to broaden the range of tissues and pathologies that can be treated using this new approach, and to validate the benefits that arise from combining the advantages of MW and chemical ablation in a relevant setting.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".