Factors Predictive of Obliteration After Arteriovenous Malformation Radiosurgery
Bibliographic record
Abstract
OBJECTIVE: To investigate predictive factors of complete obliteration following treatment with linac-based stereotactic radiosurgery for intracerebral arteriovenous malformations. METHODS: Archived plans for 48 patients treated at the British Columbia Cancer Agency and who underwent post-treatment digital subtraction angiography to assess obliteration were studied. Actuarial estimates of obliteration were calculated using the Kaplan-Meier method. Univariate and multivariate Cox proportional hazards models were used for analysis of incidence of obliteration. Log-rank test was used to search for parameters associated with obliteration. RESULTS: Complete nidus obliteration was achieved in 38/48 patients (79.2%). Actuarial rate of obliteration was 75.9% at 4 years (95% confidence interval 63.1%-88.6%). On univariate analysis, prescribed dose to the margin (p=0.002) and dose to isocentre (p=0.022) showed statistical significance. No parameters were significant in a multivariate model. According to the log-rank test, prescribed dose to the margin of >20 Gy (p=0.004) and dose to the isocentre of >25 Gy (p=0.004) were associated with obliteration. CONCLUSION: Reported series in the literature suggest a number of different factors are predictive of complete obliteration of arteriovenous malformations following radiosurgery. However, differing definitions of volume and complete obliteration makes direct comparison between series difficult. This study demonstrates that complete obliteration of the nidus following linear accelerator-based stereotactic radiosurgery for arteriovenous malformations appears to be most closely related to the prescribed marginal dose. In particular, a marginal dose of >20 Gy is strongly associated with obtaining complete obliteration of the nidus.
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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 | 0.005 |
| 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".