Novel Strategies for Expanding the Endoscopic Caudal Access to the Craniovertebral Junction: A Cadaveric Comparative Analysis of Nasofrontal Trephination and Posterior Palatectomy
Bibliographic record
Abstract
Abstract Although the endoscopic endonasal approach (EEA) has emerged as a preferred alternative to traditional transoral access for craniovertebral junction (CVJ) surgery, its limited inferior reach beyond the C1 level remains a significant challenge. This study investigates the effectiveness of using a contralateral nasofrontal trephination (CNT) or a posterior palatectomy (PP) to enhance the caudal reach to the CVJ region. A quantitative cadaveric study. Cadaver dissection laboratory. A total of 15 adult human cadaveric heads. EEA, EEA + PP, and EEA + CNT approaches to the CVJ were performed. Neuronavigation was used for objective measurements quantifying the volume of surgical freedom, surface area of deep exposure, entry point sagittal angle, and inferior reach below the odontoid process. EEA + CNT demonstrated superior surgical metrics across all parameters. Surface exposure was significantly greater with EEA + CNT (107.04 cm2) versus EEA + PP (86.26 cm2) and standard EEA (69.78 cm2; p < 0.001). The volume of surgical freedom showed similar superiority with EEA + CNT (60.21 cm3), followed by EEA + PP (34.14 cm3) and EEA (26.13 cm3; p < 0.001). Inferior reach below the odontoid (CNT: 6.35 cm; PP: 2.17 cm; EEA: 0.9 cm; p < 0.0001) and surgical trajectory angle (CNT: 50.1 degrees; PP: 21.4 degrees; EEA: 16.6 degrees; p < 0.0001) demonstrated significant stepwise improvements with each adjunct technique. Both CNT and PP techniques significantly enhance the surgical corridor of traditional EEA for CVJ access. A CNT afforded superior surgical exposure while maintaining minimal invasiveness. PP offers a viable alternative when external incisions are undesired and lesions are confined within the C2–C3 level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".