A graphical user interface for an electron monitor unit calculator using a sector-integration algorithm and exponential curve-fitting method
Bibliographic record
Abstract
A new electron monitor unit (MU) calculator program called "eMUc" was developed to provide a convenient electron MU calculation platform for the physics and radiotherapy staff in electron radiotherapy.The program was written using the Microsoft Visual Basic.netframework and has a user-friendly front-end window with the following features: (1) Apart from using the well-known polynomial curvefitting method for the interpolation and extrapolation of relative output factors (ROFs), an exponential curve-fitting method was used to obtain better results.(2) A new algorithm was used to acquire the radius in each angular segment in the irregular electron field during the sector integration.(3) A comprehensive graphical user interface running on the Microsoft Windows operating system was used.(4) Importing irregular electron cutout field images to the calculator program was simplified by using only a commercial optical scanner.(5) Interlocks were provided when the input patient treatment parameters could not be handled by the calculator database accurately.(6) A patient treatment record could be printed out as an electronic file or hard copy and transferred to the patient database.The data acquisition mainly required ROF measurements using various circular cutouts for all the available electron energies and applicators for our Varian 21 EX linear accelerator.To verify and implement the calculator, the measured results using our specific designed irregular and clinical cutouts were compared to those predicted by the calculator.Both agreed well with an error of ±2%.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.152 | 0.029 |
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".