Influence of dural attachment on remnant progression after subtotal resection in WHO grade 1 meningioma
Bibliographic record
Abstract
PURPOSE: Meningioma resection remains partial in over 25% of cases, with few known predictors of residual disease progression. This study aims to assess if dural attachment, identified on postoperative imaging, influences meningioma progression following subtotal resection. METHODS: A retrospective cohort design was applied to patients who underwent subtotal meningioma resection at our institution. MRI data collected over time included remnant volumes, surface areas of contact with dura and signal intensity ratios. Progression was defined as an increase in remnant volume of at least 25%. RESULTS: , respectively. Criteria for progression was met in 35 remnants (57.4%), with a mean time to progression of 18.1 months. Greater surface area of contact with dura relative to total remnant surface area was a significant predictor of progression in both univariate and multivariate analyses. Smaller remnant diameter and larger extent of resection were marginally associated with progression. Signal intensity ratios failed to demonstrate association with progression. CONCLUSION: The degree of meningioma remnant attachment to dura appears associated with progression. Reduction of dural involvement in residual disease intentionally left in place may be considered when appropriate, with further studies needed to refine surgical considerations.
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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 | 0.001 | 0.002 |
| 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".