Testing the radiosurgery-based arteriovenous malformation score and the modified Spetzler—Martin grading system to predict radiosurgical outcome
Bibliographic record
Abstract
Object. The aim of this study was to validate the radiosurgery-based arteriovenous malformation (AVM) score and the modified Spetzler—Martin grading system to predict radiosurgical outcome. Methods. One hundred thirty-six patients with brain AVMs were randomly selected. These patients had undergone a linear accelerator radiosurgical procedure at a single center between 1989 and 2000. Patients were divided into four groups according to an AVM score, which was calculated from the lesion volume, lesion location, and patient age (Group 1, AVM score < 1; Group 2, AVM score 1–1.49; Group 3, AVM score 1.5–2; and Group 4, AVM score > 2). Patients with a Spetzler—Martin Grade III AVM were divided into Grades IIIA (lesion > 3 cm) and IIIB (lesion < 3 cm). Sixty-two female (45.6%) and 74 male (54.4%) patients with a median age of 37.5 years (mean 37.5 years, range 5–77 years) were followed up for a median of 40 months. The median tumor margin dose was 15 Gy (mean 17.23 Gy, range 15–25 Gy). The proportions of excellent outcomes according to the AVM score were as follows: 91.7% for Group 1, 74.1% for Group 2, 60% for Group 3, and 33.3% for Group 4 (chi-square test, degrees of freedom (df) = 3, p < 0.001). Based on the modified Spetzler—Martin system, Grade I lesions had 88.9% excellent results; Grade II, 69.6%; Grade IIIB, 61.5%; and Grades IIIA and IV, 44.8% (chi-square test, df = 3, p = 0.047). Conclusions. The radiosurgery-based AVM score can be used accurately to predict excellent results following a single radiosurgical treatment for AVM. The modified Spetzler—Martin system can also predict radiosurgical results for AVMs, thus making it possible to use this system while deciding between surgery and radiosurgery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".