Interactive System for Medical Interventions Based on Magnetic Resonance Targeting
Bibliographic record
Abstract
Abstract—Magnetic Resonance Targeting (MRT) is a new approach that aims at navigating untethered therapeutic or diagnostic micro-entities through the vascular network until a specific targeted location is reached. The platform used for such intervention is referred to as a Magnetic Resonance Navigation (MRN) system and it typically takes the form of a clinical Magnetic Resonance Imaging (MRI) scanner upgraded with special software and hardware modules to allow such platform to perform MRN in an effective manner. Considering the complexity of MRN operations and the speed at which real-time operations are being performed, an interactive system capable of providing a proper interface to allow an interventional radiologist or the like to properly interact and use such platform becomes an essential, yet a critical component for the success of this new medical interventional approach. Here, this paper presents for the first time an overview of a new interactive system for MRN operations suitable to conduct preliminary interventions. Keywords-magnetic; Magnetic resonance imaging; targeted interventions; cancer therapy; user interface I.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".