Evaluation of Patients with Cranial Nerve Disorders
Bibliographic record
Abstract
Abstract Neurologists, neurosurgeons, ENT and maxillofacial surgeons, ophthalmologists, and others often detect cranial nerve deficits in their patients but remain uncertain about the underlying cause. It is the radiologist’s task to identify the causative disease, including inflammatory, infectious, vascular, traumatic, tumoral, and neurodegenerative etiologies. To detect this pathology, the neuroradiologist or head and neck radiologist must have a detailed knowledge of the anatomy of the 12 cranial nerves and available MR techniques. Furthermore, selecting the optimal sequences significantly depends on access to the patient’s history, clinical and biological data. In this chapter, emphasis will be put on employing the certain imaging techniques best suited to detect pathologies on the different parts/segments of the cranial nerves: intraaxial, extraaxial intracranial, skull base, and extracranial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".