Superficial Anastomotic Veins: Neurosurgical View Depending on 251 Cranitotomies
Bibliographic record
Abstract
BACKGROUND: Superficial anastomotic veins (SAVs) have been studied extensively but little attention has been paid to clinical studies. The aim of this study is to provide variations in the drainage patterns of SAVs depending on the intraoperative findings. METHODS: A total of 251 craniotomies due to intractable temporal lobe epilepsy were performed between 1972 and 1987 at the Montreal Neurological Institute. The courses of the three largest SAVs including the vein of Trolard (VT), vein of Labbe (VL) and superficial Sylvian vein (SSV) were studied. RESULTS: All three veins showed variable courses. The most common predominant vein was the combination of the VL + SSV. The VT and VL were frequently coursed at the level of the central vein and middle temporal vein, respectively. On the right hemisphere the SSV was the predominant type while the VL tended to be predominant on the left hemisphere. A combination of VL and SSV was predominant in patients with right and/or left hemispheric dominance. CONCLUSIONS: The SAVs showed considerable variation in their courses and it is difficult to define an exact pattern although some courses showed constant directions. Attention should be paid not to damage these veins since in a considerable number of cases a single dominant vein may be responsible for draining a majority of the lateral surface of cerebral hemisphere.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".