Perioperative predictive factors of intracranial meningioma recurrence following surgical resection
Bibliographic record
Abstract
Background: Meningiomas represent the commonest benign intracranial tumor and surgical resection is the first line treatment. Tumor recurrence after surgical resection is common. The aim of this study is to identify peri-operative predictors of meningioma recurrence following surgical resection Methods: This was a retrospective hospital-based study of all surgical cases between January 1990 and June 2014. Information regarding age, gender, peri-operative imaging parameters such as peri-tumoral edema or post-operative hemorrhage or residual, and grade were collected. Linear and volumetric measurements (of both tumor volume and volume of edema) were collected as well. Results: Overall, 464 patients were reviewed; n=154(34%) percent of patients were male. The grade distribution was: 296 (74.6%) were Grade I, 78 (19.6%) Grade II, and 23 (5.8%) Grade III. Post-operative tumor bed hemorrhage, noted in 119 (29.9%) of cases, and preoperative peri-tumoral edema volume were significant predictors of tumor recurrence following resection (P= 0.002 and 0.037, respectively). These parameters did not correlate with the MIB-1 index, tumour residual, grade of the tumour, or primary versus recurrent presentation. Conclusions: Pre-operative peri-tumoral edema and post-operative tumor bed hemorrhage are independent predictive of tumor recurrence. Identification of other molecular and/or radiological predictive of recurrence factors could add in our understanding of meningioma behavior.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".