Radiological Predictive Factors for Regrowth of Residual Benign Meningiomas
Bibliographic record
Abstract
The pre- and postoperative radiological predictive factors for the regrowth of residual benign meningiomas were investigated in 80 of 327 patients who underwent first surgery for intracranial meningioma, who met the following conditions: residual tumor observed on postoperative imaging, follow up for more than 5 years or until regrowth of the residual tumor, histological diagnosis of World Health Organization grade I, and no additional therapy performed within 1 month after surgery. These 80 patients were divided into those with no regrowth during the follow-up period (Group A, n = 54) and those with regrowth (Group B, n = 26), and the clinical characteristics and pre- and postoperative imaging findings were compared. Univariate analysis of factors influencing regrowth showed 6 factors were significant: tumor size ≥4 cm (p = 0.043), tumor volume ≥30 cm(3) (p = 0.026), presence of edema (p = 0.036), unclear brain-tumor interface (p < 0.001), presence of a pial-cortical blood supply (p = 0.031), and residual tumor volume ≥3.0 cm(3) (p < 0.001). Multivariate analysis showed only residual tumor volume ≥3.0 cm(3) was significant (p = 0.001). Generally, the significant imaging findings on univariate analysis suggest malignant meningioma. Similar findings may be observed even in grade I cases, and residual tumors may regrow in such cases. The possibility is particularly high if the residual tumor volume exceeds 3.0 cm(3), so early radiotherapy should be performed to prevent regrowth.
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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 | 0.000 | 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".