Printing in Time for Cranio-Maxillo-Facial Trauma Surgery: Key Parameters to Factor in
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it