Endoscopic Endonasal Approach to the Odontoid and Craniocervical Junction
Bibliographic record
Abstract
Background: Endoscopic endonasal approaches (EEAs) have been applied for skull base pathologies from the christa galli to the clivus. This approach also provides a natural corridor to the craniocervical junction and odontoid process. Objectives: To evaluate the surgical techniques, challenges, advantages and limitations of EEA for craniocervical pathologies. Methods: We present the endoscopic endonasal surgical technique for accessing the odontoid process and craniocervical junction. We present the advantages such as avoidance of oral mucosal transgression with its associated increased infection risk and potential swallowing delay and worsening. The caudal limits are discussed as well as methods for predicting them radiographically with the nasopalatal line. Finally, outcomes are presented to further examine the advantages and disadvantages of the transnasal approach. Conclusions: The ventral craniocervical junction and odontoid process can be addressed safely and effectively via EEA, without the attendant swallowing issues created by transoral approaches.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".