In house virtual surgery and 3D complex head and neck reconstruction
Bibliographic record
Abstract
BACKGROUND: 3-Dimensional (3D) printing can be applied to virtual planning and creation of surgical guides for mandibular reconstruction. Such systems are becoming increasingly prevalent in head and neck reconstruction. However, third party access to this technology is costly and removes the opportunity to design, create, and modify the bony reconstructions, as third party technology is a black box. This series is a pilot study to document the feasibility of an in-house software tool. The objectives of this study are to describe the design of an automated in house system and assess the accuracy of this in house automated software tool for mandibular reconstruction in a simulated environment. METHODS: Software was written to automate the preoperative planning and surgical guide creation process. In a simulation lab, Otolaryngology residents were tasked with resecting and reconstructing a simulated mandible using the 3D-printed cutting guides. A control group of residents performed resection and reconstruction using the traditional method without cutting guides. T-test analysis was performed to compare specific aspects of the final reconstructions including: change from native mandibular width and projection, segment gap distance, and reconstruction time. RESULTS: Mandibular reconstruction was successful in all participants using the 3D printed system. The guided group performed significantly better on the measurement of change in Mandibular overlap, projection, segment gap volume. There was a non-significant trend towards better mandibular width and operative time for the guided group. CONCLUSIONS: This study confirms functionality and feasibility of using an in house automated software for planning and creating surgical guides.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".