In-house 3D Model Printing for Acute Cranio-maxillo-facial Trauma Surgery: Process, Time, and Costs
Bibliographic record
Abstract
Three-dimensional (3D) printing is used extensively in cranio-maxillo-facial (CMF) surgery, but its usage is limited in the setting of acute trauma specifically, as delays in outsourcing are too great. Therefore, we developed an in-house printing solution. The purpose of this study was to describe this process for surgeons treating acute CMF trauma. This series describes the printing process, time required, and printing material costs involved for in-house printing applied to a variety of acute CMF trauma cases involving the upper, middle, and lower thirds of the face and skull. All consecutive patients requiring in-house 3D printed models in a level 1 trauma center for acute trauma surgery in mid-2019 were identified and analyzed. Nine patients requiring the printing of 12 in-house models were identified. The overall printing time per model ranged from 2 hours, 36 minutes to 26 hours, 54 minutes (mean = 7h 55 min). Filament cost was between $0.20 and $2.65 per model (mean = $0.95). This study demonstrates that in-house 3D printing can be done in a relatively short period of time, therefore allowing 3D printing usage for various acute facial fracture treatments. The rapid improvements in the usability of 3D software and printing technology will likely contribute to further adoption of these technologies by CMF-trauma surgeons.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".