Temporal Lobe Arteriovenous Malformations
Bibliographic record
Abstract
BACKGROUND: Temporal lobe arteriovenous malformations (AVMs) represent a subgroup of intracranial AVMs with particular characteristics and management issues. OBJECTIVE: To characterize the surgical outcomes of temporal lobe AVMs with emphasis on visual field deficits (VFDs) and seizures. METHODS: Between 1992 and 2008, 29 patients were operated on for temporal lobe AVMs. Patient data were retrospectively collected and analyzed. RESULTS: Twelve of 29 patients (41.4%) presented with seizures and 4 (13.7%) presented with VFDs. Postoperatively, 6 patients (24%) showed new VFDs and 2 improved, with a rate of preservation of full visual fields of 84%. Larger AVMs (> 3 cm) were significantly associated with postoperative VFD (P = .008). Epilepsy outcomes assessed by the Engel scale were as follows: 9 patients (75%) were in class I (seizure free), 1 patient (8.3%) was in class III, and 2 patients (16.6%) were in class IV (no change or worsening). Postoperative modified Rankin Scale outcomes were excellent (grade 0-1) in 18 patients, good (grade 2) in 7, and poor (grade 3-4) in 4. Older age at diagnosis correlated with a worse functional outcome (Spearman ρ = 0.369; P = .049). AVMs were totally removed in 27 of 29 patients (93.1%). Complete surgical excision was confirmed with angiography. Two patients needed reoperation for AVM remnant. Three patients had persistent hemiparesis (10.3% permanent morbidity). There was no mortality. CONCLUSION: Seizure control is usually underappreciated in the surgical management of AVMs. However, in temporal lobe AVMs, good outcomes with low morbidity and good visual field preservation can be accomplished.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".