Effectiveness of Postoperative Adjuvant Radiotherapy in Atypical Meningioma Patients After Gross Total Resection: A Meta-Analysis Study
Bibliographic record
Abstract
Background It still remains unclear whether patients with atypical meningioma (AM) could benefit from postoperative adjuvant radiotherapy (PORT) after gross-total resection (GTR). Objective Exploring the effectiveness of PORT on AM patients after GTR. Methods Literatures on PubMed, Embase, Web of science, and Scopus databases published between January 2000 and January 2019 were searched. After the selection based on the certain exclusion criteria, the Newcastle-Ottawa evaluation scale was used to evaluate the quality of the included literatures. Finally, a meta-analysis was conducted to analyze the effectiveness of PORT on local control (LC), progression-free survival (PFS) and overall survival (OS) in atypical meningioma patients after GTR. Results A total of 17 articles with 2,008 AM patients were included in the meta-analysis. The 5-year LC, 5-year PFS, and 5-year OS rates were 82.2, 84.1, and 79.0%, respectively, for AM patients receiving PORT after GTR, and they were 71.0, 71.9, and 81.5%, respectively, for those not receiving PORT after GTR. PORT could significantly improve 5-year LC rate (OR [95% Cl] = 2.59 [1.40–4.81], P = 0.002) and 5-year PFS rate (OR [95% Cl] = 1.99 [1.35–2.95], P = 0.001), but did not significantly improve 5-year OS rate (OR [95% Cl] = 1.07 [0.60–1.91], P = 0.828). Conclusion PORT could improve the 5-year LC rate and 5-year PFS rate in AM patients after GTR. AM patients might benefit from PORT after GTR.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.048 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".