Development of a novel loosely wound helical coil for interstitial radiofrequency thermal therapy
Bibliographic record
Abstract
We have developed a novel, radiofrequency thermal therapy device designed to improve local control of large solid tumours using heat in the range 55-90 degrees C. The device is a solenoid or helical coil designed to be loosely wound inside a tumour and excited with radiofrequency energy. Typically, we associate a uniform axially directed magnetic field with a solenoid coil, which when time varying, results in an electric field inside the coil, which lies mainly in the circumferential direction. In addition to this magnetically induced electric field, there exists a less familiar axially directed electric field inside the coil. Previous investigators have demonstrated the presence of this secondary axial electric field both experimentally and theoretically. Our design exploits the size and uniformity of these electric fields, for heating and coagulating a large tissue volume with a single applicator. The loosely wound solenoid is constructed from Nitinol, an electrically conductive shape memory alloy that permits the minimally invasive percutaneous insertion of the coil through a single cannulating delivery needle. To demonstrate the potential of this device and to determine the optimal frequency of operation, phantom tissue models and finite-element calculation models using COMSOL 3.2 were used to characterize frequency- and geometry-dependent trends in absorption rate density (ARD), which is proportional to electric field intensity. Radial and axial ARD profiles were measured, calculated and evaluated to determine the frequency and geometry best suited for producing large, homogenous coagulation volumes. Based on the trade-off between radial and axial uniformities of the ARD profiles, a 2 cm diameter coil with a 4 cm length and 1 cm pitch, operated at 27.12 MHz, produced the optimal heating pattern, as determined using tissue-mimicking phantom models.
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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) | 0.000 | 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 | 0.000 | 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".