Diffusion tensor imaging analysis of long association bundles in the presence of an arteriovenous malformation
Bibliographic record
Abstract
OBJECT: Conventional imaging demonstrates intertwined fibers of the cerebral white matter as a homogeneous substrate. Recently, diffusion tensor imaging has allowed 3D reconstruction of these fiber bundles. The goal of this study was to analyze the modifications of the association fibers induced by an arteriovenous malformation (AVM) in the parietotemporooccipital (PTO) associative area and their clinical significance. METHODS: The authors analyzed the long association fibers in seven patients harboring an AVM in or near the PTO region in relation with the fibers' clinical manifestation. The fibers include the arcuate fasciculus (AF), the occipitofrontal fasciculus (OFF), and the inferior longitudinal fasciculus (ILF). These structures were compared with the contralateral bundles. RESULTS: The modification of the tracts could establish a pattern signature depending on the specific location of the vascular malformation. There was a positive correlation between the degree of modifications of OFF and ILF fiber tracts and visual deficits. Alteration of the AF correlated with a speech disorder and the risk of postoperative deficits. CONCLUSIONS: Diffusion tensor imaging enables in vivo dissection of fiber tracts coursing through the PTO area. Depending on the location of the AVMs, long association fibers are variously modified. These findings correlate with clinical manifestations and may predict outcome after surgery.
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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 | 0.002 |
| 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) | 0.001 | 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".