SU‐F‐T‐535: An Android App and Windows Executable with GUI for the Monitor Unit Calculation in KV X‐Ray Therapy
Bibliographic record
Abstract
Purpose: We created two applications, one for use on Android and one for use on Windows, to carry out monitor unit (MU) calculations. These applications carried out these long calculations quickly, while avoiding the potential for human error. Methods: A general formula for calculating MU for an orthovoltage x‐ray machine was used and implemented in two programs created for Android and Windows. The formula relies on the prescribed dose and fractionation. Other values the formula relies upon are relative exposure factor (REF) and backscatter factor (BSF). These factors are specific to each unit and head in use, and are measured and tabulated prior to use. In the case of a BSF not falling on already known values, the programs were made to automatically use linear interpolation to find an appropriate BSF. The programs also allow for a stand‐off correction, calculated using the inverse‐square law, and an attenuator value that can be arbitrarily input by the user. Results: The two programs were built using C#, and use essentially the same types of backends to do their calculations. Differences in the programs are mainly in the graphical user interfaces. The backend was made to mimic the way a human does the same calculations. Previously measured REF and BSF tables for three different x‐ray energies and field size types were loaded into the program beforehand for testing. Both applications were easy to use, and produced the exact same results in all situations. These results matched the same calculations done by hand. Differences were only found when hand calculations use shortcuts to find BSF values that lie outside of the pre‐measured values. Conclusion: Our programs can efficiently and effectively calculate MU as well as or better than by hand. The Android version can be useful in the future for emergency situations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.019 |
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".