Intraoperative Image-Guided Navigation in Craniofacial Surgery: Review and Grading of the Current Literature
Bibliographic record
Abstract
INTRODUCTION: Image-guided navigation has existed for nearly 3 decades, but its adoption to craniofacial surgery has been slow. A systematic review of the literature was performed to assess the current status of navigation in craniofacial surgery. METHODS: A Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) systematic review of the Medline and Web of Science databases was performed using a series of search terms related to Image-Guided Navigation and Craniofacial Surgery. Titles were then filtered for relevance and abstracts were reviewed for content. Single case reports were excluded as were animal, cadaver, and virtual data. Studies were categorized based on the type of study performed and graded using the Jadad scale and the Newcastle-Ottawa scales, when appropriate. RESULTS: A total of 2030 titles were returned by our search criteria. Of these, 518 abstracts were reviewed, 208 full papers were evaluated, and 104 manuscripts were ultimately included in the study. A single randomized controlled trial was identified (Jadad score 3), and 12 studies were identified as being case control or case cohort studies (Average Newcastle-Ottawa score 6.8) The most common application of intraoperative surgical navigation cited was orbital surgery (n = 36), followed by maxillary surgery (n = 19). Higher quality studies more commonly pertained to the orbit (6/13), and consistently show improved results. CONCLUSION: Image guided surgical navigation improves outcomes in orbital reconstruction. Although image guided navigation has promise in many aspects of craniofacial surgery, current literature is lacking and future studies addressing this paucity of data are needed before universal adoption can be recommended.
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.036 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".