Diffusion Tensor Imaging for Ruptured Cerebral Arteriovenous Malformation
Bibliographic record
Abstract
Non-ruptured arteriovenous malformations (AVMs) rarely cause tract disruption. Few studies have described how ruptured AVMs influence white matter (WM) tract morphology. We reviewed consecutive AVM cases treated at a tertiary care hospital where diffusion tensor imaging (DTI) tractography was obtained preoperatively. DTI was performed using the Synaptive Plan (Synaptive Medical Inc., Toronto, Canada). Quality control was performed by clinical application specialist. Perinidal fractional anisotropy (FA) value of corticospinal tracts (CST) was obtained. A reference FA value was obtained from the corresponding area on the contralateral side. Images were evaluated by a consultant neuroradiologist. Radiological findings were correlated with clinical findings. White matter morphology was described by a consultant neuroradiologist. All three cases included in the study had a history of haemorrhage in the past. Two patients had disruption of CST and presented with a significant neurological deficit. In one patient FA value of CST around the nidus was comparable to the contralateral side and did not show any neurological deficit. DTI integrated neuronavigation was used to plan the trajectory and complete resection of the AVM with excellent postoperative recovery.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".