Characterization of novel 3D‐printed metal shielding for brachytherapy applicators
Bibliographic record
Abstract
PURPOSE: To characterize 3D-printed stainless steel metal samples in the presence of an Iridium-192 source for organ-at-risk sparing in gynecologic brachytherapy. METHODS: , thickness range 1-5 mm) were embedded in a solid water phantom at varying distances from source catheters. An Ir-192 brachytherapy source was passed through the phantom and the dose was measured using EBT3 Gafchromic film. The film was initially positioned in the sagittal plane 2 cm away from the catheters, with the metal directly below and then 1 cm from the film. A uniform dose was delivered at the film plane. A second setup measured a depth dose curve in solid water with film in the transverse plane directly above the metal samples. This setup was recreated using Monte Carlo simulations (EGSnrc egs_brachy). Validation between methods was performed with unshielded (solid water only) measurements. RESULTS: The planar dose passing through the metal samples (thickness 1-5 mm) at the midpoint between the film and catheters, decreased compared to solid water by 7.4% ± 6.9% to 26.5% ± 5.5%. Dose enhancement on the order of 5% was noted when metal was directly adjacent to the film. The average decrease in depth dose from a single dwell position ranged from 10.0% ± 5.9% (1 mm) to 21.1% ± 5.3% (5 mm) as measured with film, and from 3.8% ± 0.9% (1 mm) to 16.3% ± 0.9% (5 mm) using MC simulation. The average depth dose values were measured using a line width of 2.5 mm for film, and 3 mm for MC simulation, and the measurements generally agree within standard error. CONCLUSIONS: The 3D-printed metal samples show potential for personalized applicators. Maximum dose reduction of 26.5% ± 5.5% compared to solid water was measured at 2 cm from the source using the 5 mm sample. An outer layer of solid water could potentially be used to reduce dose enhancement due to increased scatter near the metal.
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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 | 0.000 | 0.001 |
| 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".