Effective Source-Surface Distance in Various Field Sizes and Electron Beam Energies and its Effect on Cutout Factor in a Elekta Precise Linear Accelerator
Bibliographic record
Abstract
Introduction: In electron beam treatment, because of the non-point electron beam source, inverse-square law cannot be applied for dosimetry in different treatment intervals. Therefore, providing source-surface distance (SSD) charts in all clinics is of paramount importance. This study aimed to determine the effective SSD for various electron beam energies and field sizes and to evaluate its effect on cutout factor in a linear accelerator. Materials and Methods: We used Elekta Precise linear accelerator in Ayatollah Khansari Hospital, Arak, Iran, for various energy levels (10, 15, and 18 MeV). The measurement environment was MP3-M water phantom (PTW Co., Canada), and diode detector was utilized for dosimetry. The effective SSD and cutout factor was estimated for 100, 105, 110, 115, and 120 cm SSDs and 1.5×1.5 and 20×20 cm2 square fields. Results: The effective SSD in the 1.5×1.5 to 20×20 cm2 fields altered from 29.95 to 93.95 cm, 50.40 to 96.50 cm, and 63.51 to 95.32 cm for different energy levels of 10 MeV, 15 MeV, and 18 MeV, respectively. The cutout factor increased along with the field size, but decreased by extending the SSD. These alterations were more significant for the energy level of 10 MeV. Conclusion: Since the effective SSD is dependent on energy level and field size, it is recommended to independently compute the effective SSD considering these variables. Furthermore, for designing accurate therapies, cutout factor variations should be considered for small-sized fields, especially at low energy levels.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".