From conception to clinical trial: IViST, the first multi-sensor-based platform for real-time In Vivo Source Tracking in HDR brachytherapy
Bibliographic record
Abstract
Abstract This study is aimed to introduce a novel multi-sensor-based dosimetry platform for real-time plan monitoring in HDR brachytherapy: IViST (In Vivo Source Tracking). IViST is a platform composed of three components: 1) an optimized and characterized multi-point plastic scintillator dosimeter (3 points mPSD; using BCF-60, BCF-12, and BCF-10 scintillators), 2) a compact assembly of photomultiplier tubes (PMTs) coupled to dichroic mirrors and filters for high-sensitivity scintillation light collection, and 3) a Python-based graphical user interface used for system management and signal processing. IViST can simultaneously measure dose, triangulate source position, and measure dwell time. By making 100 000 measurements/s, IViST samples enough data to quickly perform key QA/QC tasks such as identifying wrong individual dwell time, position, or interchanged transfer tubes. By using 3 co-linear sensors and planned information for an implant geometry, the platform can also triangulate source position in real-time. A clinical trial to validate this system is presently on-going using the IViST system.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".