Magnetic resonance imaging of the cranial nerves in infectious, neoplastic, and demyelinating diseases, as well as other inflammatory diseases: a pictorial essay
Bibliographic record
Abstract
The cranial nerves, which represent extensions of the functional structures of the brain, traverse the head and neck. They are connected to various cranial structures and are associated with several diseases. An in-depth understanding of their complex anatomy and normal imaging appearance allows the examiner to identify and characterize abnormalities with greater precision. One important tool for evaluating the cranial nerves is contrast-enhanced magnetic resonance imaging, especially that employing three-dimensional steady-state free precession sequences, which provide high soft-tissue and spatial resolution, despite the slen-derness of the nerves. In most cases of cranial nerve abnormalities, the imaging findings are nonspecific. Therefore, to narrow the differential diagnosis, it is necessary to take a full patient history, perform a focused physical examination, and order laboratory tests. In this pictorial essay, we review, illustrate, and discuss, from a pathophysiological perspective, infectious, neoplastic, and demyelinating disorders, as well as other inflammatory disorders, affecting the cranial nerves, the aim being to provide a practical, tangible reference for radiologists to use in daily practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".