Initial characterization of a novel dual-robot orthovoltage radiotherapy system
Bibliographic record
Abstract
Abstract Purpose: Adequate access to radiotherapy is a critical global concern affecting low-resource settings such as low- and middle-income countries and rural regions. We propose to reduce this disparity by developing a novel low-cost radiotherapy device that treats using non-coplanar techniques and a 225 kVp x-ray tube. Methods: This novel device has been preliminarily characterized spectrally, via spectrometer measurements, dosimetrically, via percent depth dose curves and 2D profiles, and geometrically, via a coplanar star-shot. Dosimetric and geometric evaluations were then combined by performing a proof of workflow of the KOALA system. Monte Carlo simulations were run in TOPAS to validate dosimetric measurements and the proof of workflow measurement. Results: Spectral results showed excellent agreement between measured and modelled spectra. Dose errors of < 2% were achieved for PDD curves. Full width at half maximum values for the 2D profiles were, on average, 0.95 mm higher in simulation compared to film. A star-shot test demonstrated the high geometrical accuracy of the system with a 0.3 mm diameter wobble circle. Finally, a mean absolute percent error of 5 ± 5% (1 σ ) was measured for the proof of workflow test. Conclusions: This initial characterization showcased the strengths and weaknesses of the KOALA system, with excellent isocenter precision and depth dose accuracy while lacking dosimetric accuracy in the 2D profiles. Further improvements on the source-to-collimator distance and treatment couch material can be made to improve the accuracy of a Monte Carlo model of the KOALA 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".