Atypical Meningioma: Referral Patterns, Treatment and Adherence to Guidelines
Bibliographic record
Abstract
OBJECTIVE: To determine the referral rate to radiation oncologist (RO), use of postoperative radiotherapy (PORT) and the impact of a clinical practice guideline (CPG) on patients with atypical meningioma (AM). METHODS: A retrospective review of meningioma patients (n=526) treated between 2003 and 2013 was undertaken. Patients' characteristics, extent of surgical resection (EOR), RO referral, PORT, date and treatment of first recurrence were collected for all patients >18 years with a new diagnosis of AM after surgical resection (n=83). Progression free survival (PFS) and overall survival (OS) according to EOR were assessed by the Log-Rank test of Kaplan-Meier survival. RESULTS: Median age was 57 years. EOR was gross total (GTR) in 44 patients, subtotal (STR) in 36 patients and 3 patients had unknown EOR. RO referral rate was 26.5% (n=22); 5 patients initially had GTR and 17 had STR. Only 7 patients received PORT. At a median follow up time of 29 months, recurrences occurred in 28 patients, 4 had GTR, 21 had STR and 3 had an unknown EOR. With PORT, 2 patients developed recurrence. 5-year PFS was 62% after GTR and 33% after STR (P=0.002). 5-year OS was 92% after GTR and 83% after STR (P=0.45). CONCLUSION: In this cohort with AM, RO referral rate was low and was not influenced by the CPG. Use of PORT was also low. Given the lack of conclusive evidence supporting PORT in such patients, a multidisciplinary approach, including RO consultation, is needed to provide patients with optimal and individualised care.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".