Evolution of Diagnostic Neuroradiology from 1904 to 1999
Bibliographic record
Abstract
Neuroradiology began in the early 1900s soon after Roentgen discovered x rays, with the use of skull radiographs to evaluate brain tumors. This was followed by the development of ventriculography in 1918, pneumoencephalography in 1919, and arteriography in 1927. In the beginning, air studies were the primary modality, but this technique was supplanted by angiography in the 1950s and 1960s. The first full-time neuroradiologist in the United States was Cornelius G. Dyke at the New York Neurological Institute in 1930. Neuroradiology took a firm hold as a specialty in the early 1960s when Dr Juan M. Taveras brought together fourteen neuroradiologists from the United States and Canada to establish the nucleus of what was to become the American Society of Neuroradiology, or ASNR. This society's initial goals were to perform research and to advance knowledge within the specialty. Neuroradiologists initially were able to diagnose vascular disease, infections, tumors, trauma, and alterations in cerebrospinal fluid flow, because the brain structure was invisible. Neuroradiology was forever changed with computed tomography (CT) because the brain structure became visible. Soon thereafter, magnetic resonance (MR) imaging was developed, and it not only provided anatomic but also made possible vascular and physiologic functional imaging.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".