Po‐Thur Eve General‐32: Evaluation of calculated total scatter factor based on predicted collimator scatter factor and phantom scatter factor for kilovoltage x‐ray radiation fields
Bibliographic record
Abstract
Kilovoltage x‐ray tube therapy beams are used for treatment of keloids and localized superficial malignancies. The purpose of this work is to calculate the total scatter factor for kilovoltage x‐ray radiation fields based on the predicted collimator scatter factor and the predicted phantom scatter factor. The radiation field is defined by a variable collimator with or without lead sheets on the patient's skin. Measurement and prediction have been done at tube potentials of 60, 100, 120 and 250 kVp. Our approach uses three different sets of field types: square fields defined by the collimator and square and circular fields defined by lead sheets. Two calculation methods are employed: the equivalent field method and Clarkson's method. Calculation and measurement are also done for rectangular and irregular shape fields. It is shown that while the equivalent field method may lead to errors up to about 6% for irregular fields, Clarkson's method is more accurate with errors up to 2.3%. The choice of the field type for the data base is not significant. Using the measured data for square fields defined by the collimator together with Clarkson's method is recommended. The implementation of this method requires a minimum number of measurements, which are acquired during the commissioning of the unit and can be applied in dose calculation for a variety of field shapes and sizes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".