Surgically Resected Skull Base Meningiomas Demonstrate a Divergent Postoperative Recurrence Pattern Compared with Superficial Meningiomas
Bibliographic record
Abstract
Objective: This study aims to identify differences in the recurrence pattern of surgically resected skull base meningiomas compared with superficial intracranial meningiomas. Methods: This study was a retrospective hospital-based analysis of all patients referred to our institution from January 1990 to June 2014 for surgical resection of meningiomas. The database constituted both patients with a first time presentation and those with evidence of recurrence presenting for a surgical evaluation. Tumor proliferation index (based on the MIB-1 index) and the overall time to recurrence of the cohort of surgically resected skull base and superficial meningiomas were documented. Kaplan–Meier curves and life tables were constructed for analysis of survival. SPSS v22.0 was used for statistical analysis. Results: Overall, 398 intracranial meningiomas—269 (68%) superficial and 129 (32%) skull base—were available for review. Follow-up time ranged from 1 week to 250 months. Skull base lesions were found to have a significantly lower average MIB-1 index compared with their superficial counterparts (0.0413 vs. 0.0620, p = 0.001). Meningiomas in all the locations demonstrated a recurrence rate of 30% at an average of 100 months of follow-up. Subsequent to this point, however, the recurrence of skull base meningiomas demonstrated a plateau (250 months of follow-up) whereas superficial lesions were found to have a recurrence rate of 80% at 230 months of follow-up ( p = 0.038). Conclusion: As reflected by the difference in the MIB-1 index, surgically resected skull base meningiomas demonstrated a less aggressive behavior compared with superficial lesions. This was reflected in the clinical recurrence rate of 80% for the latter. This suggests that while skull base lesions may not necessarily need to be followed beyond 100 months, superficial meningiomas would require a greater long-term follow-up given their higher propensity for recurrence. Molecular markers such as the MIB-1 index are important parameters in this decision as well.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".