Evaluations of patient-specific bolus fabricated by mold-and-cast method using computer numerical control machine tools
Bibliographic record
Abstract
The patient-specific bolus fabricated by a mold-and-cast method using a 3D printer (3DP) and silicon rubber has been adopted in clinical practices. Manufacturing a mold using 3DP, however, can cause time delays due to failures during the 3D printing process. Thereby, we investigated an alternative method of the mold fabrication using computer numerical control (CNC) machine tools. Treatment plans were conducted concerning a keloid scar formed on the ear and nose. The bolus structures were determined in a treatment planning system (TPS), and the molds were fabricated using the same structure file but with 3DP and CNC independently. Boluses were then manufactured using each mold with silicone rubbers. We compared the geometrical difference between the boluses and the planned structure using computed tomography (CT) images of the boluses. In addition, dosimetric differences between the two measurements using each bolus and the differences between the measured and calculated dose from TPS were evaluated using an anthropomorphic head phantom. Geometrically, the CT images of the boluses fabricated by the 3DP mold and the CNC mold showed differences compared to the planned structure within 2.6 mm of Hausdorff distance. The relative dose difference between the measurements using either bolus was within 2.3%. In conclusion, the bolus made by the CNC mold benefits from a stable fabricating process, retaining the performance of the bolus made by the 3DP mold.
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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) | 0.000 | 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) | 0.001 | 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".