Evaluation of lens dose from anterior electron beams: comparison of Pinnacle and Gafchromic EBT3 film
Bibliographic record
Abstract
Lens dose is a concern during the treatment of facial lesions with anterior electron beams. Lead shielding is routinely employed to reduce lens dose and minimize late complications. The purpose of this work is twofold: 1) to measure dose pro-files under large-area lead shielding at the lens depth for clinical electron energies via film dosimetry; and 2) to assess the accuracy of the Pinnacle treatment planning system in calculating doses under lead shields. First, to simulate the clinical geometry, EBT3 film and 4 cm wide lead shields were incorporated into a Solid Water phantom. With the lead shield inside the phantom, the film was positioned at a depth of 0.7 cm below the lead, while a variable thickness of solid water, simulating bolus, was placed on top. This geometry was reproduced in Pinnacle to calculate dose profiles using the pencil beam electron algorithm. The measured and calculated dose profiles were normalized to the central-axis dose maximum in a homogeneous phantom with no lead shielding. The resulting measured profiles, functions of bolus thickness and incident electron energy, can be used to estimate the lens dose under various clinical scenarios. These profiles showed a minimum lead margin of 0.5 cm beyond the lens boundary is required to shield the lens to ≤ 10% of the dose maximum. Comparisons with Pinnacle showed a consistent overestimation of dose under the lead shield with discrepancies of ~ 25% occur-ring near the shield edge. This discrepancy was found to increase with electron energy and bolus thickness and decrease with distance from the lead edge. Thus, the Pinnacle electron algorithm is not recommended for estimating lens dose in this situation. The film measurements, however, allow for a reasonable estimate of lens dose from electron beams and for clinicians to assess the lead margin required to reduce the lens dose to an acceptable level.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".