Endoscopic Endonasal Approach for Resection of Cranial Base Chordomas
Bibliographic record
Abstract
BACKGROUND: Gross total resection (GTR) of cranial base chordomas represents a surgical challenge because of the location, invasiveness, and tumor extension. In the past decade, the endoscopic endonasal approach (EEA) has been used with notable outcomes. OBJECTIVE: To present the endoscopic endonasal experience in the treatment of cranial base chordomas at our institution. METHODS: From April 2003 to March 2011, 60 patients underwent an EEA for primary (n = 35) or previously treated (n = 25) cranial base chordomas. We evaluated the degree of GTR and complications. We studied the factors that influenced outcomes and compared our surgical results in the early and late years of our experience. RESULTS: The overall rate of GTR of cranial base chordomas was 66.7% (82.9% in primary and 44% in previously treated patients). The most important limitations for GTR were tumor volume greater than 20 cm (P = .042), tumor location in the lower clivus with lateral extension (P = .022), and previously treated disease (P = .002). The learning curve had a significant impact on GTR, increasing the success rate to 88.9% (92.6% in primary patients and 63.6% in previously treated patients) during recent years (P < .0001). The most frequent complication was cerebrospinal fluid leak (20%) resulting in meningitis in 3.3%. Carotid injuries occurred in 2 patients without any resulting deficit. Neurological complications included new cranial neuropathies (6.7%) and long tract deficits (1.7%). There was no operative mortality in our series. CONCLUSION: For the treatment of cranial base chordomas, the EEA is a competitive alternative to transcranial approaches with minimal morbidity and high success rates of GTR when performed by experienced cranial base surgeons.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".