Recurrence pattern of surgically-resected skull base versus superficial meningiomas, signs of divergent pattern
Bibliographic record
Abstract
Background: To identify differences in the recurrence pattern of surgically-resected skull base meningiomas compared with superficial intra-cranial meningiomas Methods: A retrospective hospital-based study of all patients referred to our institution from 1990 to 2014 for surgical resection of meningiomas was conducted (both primary and recurrent cases). Survival analysis was performed using IBM SPSS v22.0. Results: Overal, 398 intra-cranial meningiomas –129 (32%) skull base - were reviewed. Skull base tumors had a lower MIB-1 index (p = 0.001) and were more likely to be WHO I (p = 0.003). Meningiomas in all locations demonstrated a recurrence rate of 30% at 100 months of follow-up. Afterwards, the recurrence of skull base meningiomas plateaued (longest follow-up: 250 months) whereas superficial lesions had a recurrence rate of 80% at 230 months (p = 0.02). In multivariable analysis, patients with a first-time diagnosis (p = 0.02), those with WHO I or II tumors (p= 0.02 and 0.05), and those with a total resection (p < 0.01) were less likely to experience a recurrence. Conclusions: Skull base meningiomas are less aggressive than superficial lesions and may not need to be followed beyond 100 months. The WHO grade, complete resection, and prior recurrence are predictive factors of recurrence.
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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 | 0.002 |
| 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".