Intraoperative surgical navigation improves margin status in advanced malignancies of the anterior craniofacial area: A prospective observational study with systematic review of the literature and meta-analysis
Bibliographic record
Abstract
The current scientific evidence suggests that surgical navigation (SN) can contribute to improve oncologic outcomes in sinonasal and craniofacial surgery. The present study investigated the feasibility of intraoperative SN and its role in improving the outcomes of surgically treated sinonasal and craniofacial tumors. This prospective study compared navigation-guided surgery for sinonasal or craniofacial malignancies with a pair-matched cohort (1:2 matching) of patients operated without SN. A systematic review of the literature was performed. Thirty-five patients who underwent navigation-guided surgery were included. The pair-matched control cohort included 70 patients operated without SN. The margin status analysis demonstrated a lower rate of positive margins (p = 0.013) in the SN group, especially in pT4 (p = 0.034), recurrent (p = 0.024), high-grade tumors (p = 0.043), and endoscopic-assisted open surgery (p = 0.035). The mean preoperative time did not show a significant difference between surgeries performed with or without SN (1.26 vs. 1.23 h, p = 0.445). However, surgeries utilizing SN had a significantly longer median duration compared to those without (8.10 vs. 6.00 h, p = 0.029). A total of 209 patients were included in the meta-analysis; 91 patients (43.5 %) underwent surgery with SN. The results of the meta-analysis showed an improvement in terms of negative margins rate with the use of SN (OR = 2.62; 95%-confidence interval: 1.33-5.17). In conclusion, intraoperative SN can contribute to achieve a clear margin resection, especially in locally advanced tumors, recurrences, highly aggressive histologies, and when endoscopic-assisted open surgery is employed.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".