Focused ultrasound treatment of VX2 tumors controlled by local harmonic motion
Bibliographic record
Abstract
The purpose of this study was to evaluate the feasibility of using localized harmonic motion (LHM) to monitor and control focused ultrasound surgery (FUS) in VX2 tumors in vivo. FUS exposures were performed on 13 VX2 tumors implanted in nine rabbits. The same transducer induced coagulation and generated a localized oscillatory motion by periodically varying the radiation force. A separate diagnostic ultrasound transducer tracked motion by cross-correlating echo signals at different instances. A threshold in motion amplitude was instituted to cease exposure. Coagulation was confirmed by T2-weighted MR images, thermal dose obtained through MR thermometry and histological examinations. For tumor locations achieving coagulation, the LHM amplitude was 9% (p = 0.04) to 57% (p < 0.0001) lower than that before exposure. Control was successful for 74 (69%) out of 108 cases, with 52 (48%) reaching the threshold and achieving coagulation and 22 (21%) never reaching threshold nor coagulating. For the 34 (31%) unsuccessful exposures, 16 (15%) never reached the threshold but coagulation occurred, and 18 (16%) reached threshold without coagulation confirmed. Noise or radio-frequency signal changes explained motion over- or underestimation in 24 (22%) cases; the remaining 10 (9%) had other causes of error. The control was generally successful, but sudden change or noise in the acquired echo signal caused failure. Coagulation after exposure could be validated by comparing amplitudes before and after exposure.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".