Printing in Time for Cranio-Maxillo-Facial Trauma Surgery: Key Parameters to Factor in
Bibliographic record
Abstract
Study Design: retrospective cohort study. Objective: 3D printing is used extensively in cranio-maxillo-facial (CMF) surgery, but difficulties remain for surgeons to implement it in an acute trauma setting because critical information is often omitted from reports. Therefore, we developed an in-house printing pipeline for a variety of cranio-maxillo-facial fractures and characterized each step required to print a model in time for surgery. Methods: All consecutive patients requiring in-house 3D printed models in a level 1 trauma center for acute trauma surgery between March and November 2019 were identified and analyzed. Results: Sixteen patients requiring the printing of 25 in-house models were identified. Virtual Surgical Planning time ranged from 0h 08min to 4h 41min (mean = 1h 46min). The overall printing phase per model (pre-processing, printing, and post-processing) ranged from 2h 54min to 27h 24min (mean = 9h 19min). The overall success rate of prints was 84%. Filament cost was between $0.20 and $5.00 per model (mean = $1.56). Conclusions: This study demonstrates that in-house 3D printing can be done reliably in a relatively short period of time, therefore allowing 3D printing usage for acute facial fracture treatment. When compared to outsourcing, in-house printing shortens the process by avoiding shipping delays and by having a better control over the printing process. For time-critical prints, other time-consuming steps need to be considered, such as virtual planning, pre-processing of 3D files, post-processing of prints, and print failure rate.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".