Schedule feasibility and workflow for additive manufacturing of titanium plates for cranioplasty in canine skull tumors
Bibliographic record
Abstract
BACKGROUND: Additive manufacturing has allowed for the creation of a patient-specific custom solution that can resolve many of the limitations previously reported for canine cranioplasty. The purpose of this pilot study was to determine the schedule feasibility and workflow in manufacturing patient-specific titanium implants for canines undergoing cranioplasty immediately following craniectomy. RESULTS: Computed tomography scans from patients with tumors of the skull were considered and 3 cases were selected. Images were imported into a DICOM image processing software and tumor margins were determined based on agreement between a board-certified veterinary radiologist and veterinary surgical oncologist. Virtual surgical planning was performed and a bone safety margin was selected. A defect was created to simulate the planned intraoperative defect. Stereolithography format files of the skulls were then imported into a plate design software. In collaboration with a medical solution centre, a custom titanium plate was designed with the input of an applications engineer and veterinary surgery oncologist. Plates were printed in titanium and post-processed at the solution centre. Total planning time was approximately 2 h with a manufacturing time of 2 weeks. CONCLUSIONS: Based on the findings of this study, with access to an advanced 3D metal printing medical solution centre that can provide advanced software and printing, patient-specific additive manufactured titanium implants can be planned, created, processed, shipped and sterilized for patient use within a 3-week turnaround.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".