Bibliographic record
Abstract
MNI). Elvidge introduced cerebral angiography in North America in 1934, after learning the technique from Egas Moniz in Lisbon; and he published the first North-American monograph on its use-most notably as an aid in the diagnosis of arteriovenous malformations (AVM)-in 1937. He was also the first to demonstrate the occlusion of an internal carotid artery by angiography, and to correlate it with contralateral hemiplegia. Elvidge was the first neurosurgeon to clip an anterior circulation aneurysm, in 1946. 6 Francis Echlin, working toward a Master's degree under Penfield's supervision, discovered that experimental vasospasm can result in cortical infarction, in 1939. Eric Peterson and I, at the University of Ottawa, were the first to describe a chronic experimental model of subarachnoid hemorrhage, in cats and monkeys, in 1973. Peterson was also the first to treat a carotid-cavernous fistula by endovascular means, in 1969, reaching the lesion through the ophthalmic vein. illiam Feindel, Lucas Yamamoto, and Charles Hodge, from the MNI, developed the techniques of intraoperative fluorescein angiography and intraoperative radioisotope blood flow studies, which they used as an aid in the treatment of cerebral AVMs. Using these techniques they described the intra-cerebral steal syndrome associated with these lesions. Leblanc and his collaborators at the MNI quantitated the hemodynamic and metabolic aspects of the intra-cerebral steal syndrome using positron emission tomography, and Leblanc and Meyer were the first to demonstrate the usefulness of functional imaging in the treatment of AVMs. urther details on the contributions of the MNI in the field of neurovascular surgery, and of neurology and neurosurgery in general, can be found in Feindel and Leblanc, The Wounded Brain
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.281 | 0.067 |
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".