SU-FF-T-109: CNC Milling Based Immobilization Mould Fabrication
Bibliographic record
Abstract
Purpose: To increase the effectiveness, reduce cost and improve the efficiency of production of immobilization moulds Method and Materials: A process was developed to produce immobilization moulds from CT simulation data sets using Computer Numerical Controlled (CNC) milling techniques. The process demonstrated that custom moulds could be manufactured post simulation. The mould making process began with the patient undergoing a conventional planning CT scan in the treatment position but without a custom mould. External contour data was extracted from each slice with subsequent processing splitting each contour into an anterior and posterior segment. The anterior segment data and the posterior segment data were converted into surface renderings and used by a CNC to carve models of the patient from styrofoam, producing a “positive” presentation of the anterior surface and a “negative” of the posterior surface. UVEX plastic was then vacuum-formed to the “positive” model. Results: An immobilization device was manufactured for a patient undergoing cranial radiotherapy. The UVEX “positive” and styrofoam “negative” combined to form a “clam-like” device offering the combined attributes of conventional aquaplast and vac lock bags. The resulting immobilization placed the patient in exactly the same position they had assumed during simulation, ensuring a faithful planning process (i.e. the device accomplished the same requirements as an accessory produced prior to and used during simulation). However, the new approach transformed a serial process into a parallel process, with mould manufacturing and planning occurring simultaneously. The new production process did not involve physical contact with the patient. The cost to produce the mould was significantly less than that of aquaplast based techniques. Conclusion: A technique was developed to produce immobilization devices consisting of a UVEX mould and styrofoam cradle to create a “clam-like” accessory. Production was accomplished without patient contact. The process offered both cost and production time savings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".