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Record W4249124057 · doi:10.1177/159101990601200301

Neurovascular Radiosurgery

2006· article· en· W4249124057 on OpenAlexaff
Michael Söderman, Wei Guo, Bengt Karlsson, David M. Pelz, Elfar Úlfarsson, Tommy Andersson

Bibliographic record

VenueInterventional Neuroradiology · 2006
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations Diagnosis and Treatment
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineRadiosurgeryNeurovascular bundleRadiologySurgeryRadiation therapy

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.257
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2006
Admission routes1
Has abstractyes

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