Exploratory benchtop study evaluating the use of surgical design and simulation in fibula free flap mandibular reconstruction
Bibliographic record
Abstract
BACKGROUND: Surgical design and simulation (SDS) is a useful tool to help surgeons visualize the anatomy of the patient and perform operative maneuvers on the computer before implementation in the operating room. While these technologies have many advantages, further evidence of their potential to improve outcomes is required. The present benchtop study was intended to identify if there is a difference in surgical outcome between free-hand surgery completed without virtual surgical planning (VSP) software and preoperatively planned surgery completed with the use of VSP software. METHODS: Five surgeons participated in the study. In Session A, participants were asked to do a free-hand reconstruction of a 3d printed mandible with a defect using a 3d printed fibula. Four weeks later, in Session B, the participants were asked to do the same reconstruction, but in this case using a preoperatively digitally designed surgical plan. Digital registration computer software, hard tissue measures and duration of the task were used to compare the outcome of the benchtop reconstructions. RESULTS: The study revealed that: (1) superimposed images produced in a computer aided design (CAD) software were effective in comparing pre and post-surgical outcomes, (2) there was a difference, based on hard tissue measures, in surgical outcome between the two scenarios and (3) there was no difference in the time it took to complete the sessions. CONCLUSION: The study revealed that the participants were more consistent in the preoperatively digitally planned surgery than they were in the free hand surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".