Evaluation of the current radiofrequency ablation systems using axiomatic design theory
Bibliographic record
Abstract
This article evaluates current radiofrequency ablation systems using axiomatic design theory. Due to its minimally invasive procedure, short-time hospital stay, low cost, and tumour metastasis treatment, the radiofrequency ablation technique has been playing an important role in tumour treatment in recent decades. Although the radiofrequency ablation technique has many advantages, some issues still need to be addressed. Among these issues, the two most important are as follows: (1) the size of tumours to be removed (has to be larger than 3 cm in diameter) and (2) cleanness of the removal. Many device solutions have been proposed to address the two issues. However, there is a lack of knowledge regarding the systematic evaluation of these solutions. This article evaluates these systems in terms of their solution principles (or simply called conceptual design in general product design theory) using a design theory called axiomatic design theory. In addition, with the axiomatic design theory, a better conceptual design in terms of its feasibility to cope with incomplete target tissue necrosis from the large size of tumours has been found. The detailed analysis and simulation of the new conceptual design are conducted using finite element approach. The results in this article are proved by the information of animal experiments and clinical practices obtained from the literature. This study thus contributes to the current knowledge to further developments in radiofrequency ablation systems and procedure guidelines for physicians to perform the radiofrequency ablation operation more effectively.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".