Neurovascular Radiosurgery
Bibliographic record
Abstract
SUMMARY: This article focuses on the treatment of neurovascular diseases, in particular brain arteriovenous malformations (BAVMs), with radiosurgery. The target group for this review is physicians who manage patients with neurovascular diseases, but are not actively engaged in radiosurgery. Radiosurgery for BAVMs is an established treatment with clearly defined risks and benefits. The efficacy of radiosurgery for dural arteriovenous shunts (DAVSs) is probably similar but the treatment has not yet gained the same acceptance. Radiosurgical treatment of cavernomas (cavernous hemangiomas) remains controversial. Well founded predictive models for BAVM radiosurgery show: * The probability of obliteration depends on the dose of radiation given to the periphery of the BAVM. * The risk of adverse radiation effects depends on the total dose of radiation, i.e. the amount of energy imparted into the tissue. The risk is greater in centrally located lesions. The risk of damage to brainstem nucleii and cranial nerves must be added to the risk predicted from current outcome models. * The risk of hemorrhage during the time span before obliteration depends on the BAVM volume, the dose of radiation to the periphery of the lesion and the age of the patient. Central location is a probably also a risk factor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.020 |
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".