P.154 The endoscopic endonasal transclival approach for the treatment of skull base lesions
Bibliographic record
Abstract
Background: Surgical access to the clival region is challenging, but advanced endoscopic endonasal approaches (EEA) provide a minimally invasive corridor. This study aimed to review the clinical outcomes of patients who underwent EEA for skull base lesions involving the clivus and to analyze prognostic factors. Methods: A retrospective review was conducted of patients who underwent EEA for resection of clival lesions between October 2001 and October 2023. Data on demographics, approach type, reconstruction technique, tumor pathology and outcomes were collected. Results: Forty-six patients underwent transclival EEA. The majority had ASA scores II and III (71.7%), with clival chordomas being the most common pathology (37%). Cranial nerve impairment was present in 65% of patients, and 80% showed improvement post-surgery. The mean procedure duration was 308 minutes, with a mean blood loss of 424 mL. A lumbar drain was used in 10.9%, and 76.1% received a pedicled nasoseptal flap for reconstruction. Complete tumor resection was achieved in 74% of malignant cases. Postoperative CSF leaks occurred in 4.3%, and the mean length of stay was 12.2 days. Higher readmission rates were associated with ASA IV classification (p=0.006). Conclusions: EEA to the clival region is safe and effective, with low perioperative complications and high rates of postoperative improvement.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".