Intraoperative surgical navigation as a precision medicine tool in sinonasal and craniofacial oncologic surgery
Bibliographic record
Abstract
INTRODUCTION: Recent evidence supports the efficacy of surgical navigation (SN) in improving outcomes of sinonasal and craniofacial oncologic surgery. This study aims to demonstrate the utility of SN as a tool for integrating surgical, radiologic, and pathologic information. Additionally, a system for recording and mapping biopsy samples has been devised to facilitate sharing of spatial information. MATERIALS AND METHODS: SN was utilized for biopsy mapping in 10 sinonasal/craniofacial oncologic procedures. Twenty-five raters with experience in anterior skull base oncology were interviewed to identify 15 anatomical structures in preoperative imaging, relying on topographical descriptions and surgical video clips. The difference in the localization of anatomical structures by raters was analyzed, using the SN-mapped coordinates as a reference (this difference was defined as spatial error). RESULTS: The analysis revealed an average spatial error of 9.0 mm (95 % confidence interval: 8.3-9.6 mm), with significant differences between surgeons and radiation oncologists (7.9 mm vs 12.5 mm, respectively, p < 0.0001). The proposed model for transferring SN-mapped coordinates can serve as a tool for consultation in multidisciplinary discussions and radiotherapy planning. CONCLUSIONS: The current standard method to evaluate disease extension and margin status is associated with a spatial error approaching 1 cm, which could affect treatment precision and outcomes. The study emphasizes the potential of SN in increasing spatial precision and information sharing. Further research is needed to incorporate this method into a multidisciplinary workflow and measure its impact on outcomes.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".