Pathology of the conus medullaris and cauda equina. Beyond the usual suspects
Bibliographic record
Abstract
BACKGROUND: Pathologies affecting the conus medullaris and cauda equina can present with overlapping clinical symptoms, making an accurate diagnosis essential. Conus medullaris syndrome results from damage at the T12-L2 level, while cauda equina syndrome arises from nerve root compression below the conus. Both conditions may cause motor deficits, sensory disturbances, and autonomic dysfunction, necessitating a detailed differential diagnosis. OBJECTIVE: This educational review highlights common and rare etiologies of conus medullaris and cauda equina lesions, emphasizing imaging characteristics and diagnostic considerations. A comprehensive review of tumors, infections, inflammatory, vascular, and degenerative conditions affecting these regions was performed. Contrast-enhanced MRI was identified as the gold standard for diagnosis. REVISED PATHOLOGIES: Tumors: myxopapillary ependymomas and schwannomas are the most frequent neoplasms, while drop metastases and glioblastomas represent rarer entities. INFECTIONS: tuberculous arachnoiditis, bacterial radiculitis, schistosomiasis, and neurocysticercosis may mimic neoplastic processes. Inflammatory disorders: Guillain-Barré syndrome, neurosarcoidosis, and MOGAD may cause nerve root thickening and enhancement. Vascular lesions: spinal dural arteriovenous fistulas, infarcts, and arteriovenous malformations can produce conus and cauda equina symptoms. Miscellaneous causes: developmental anomalies like diastematomyelia and ventriculus terminalis, along with degenerative diseases, can mimic other conditions. CONCLUSION: Radiologists play a pivotal role in differentiating conus medullaris and cauda equina pathologies. A thorough understanding of imaging findings is essential for accurate diagnosis and effective management. CRITICAL RELEVANCE STATEMENT: Conus medullaris and cauda lesions present with overlapping clinical symptoms but show some distinct imaging patterns. It is essential to recognize characteristic features that differentiate neoplastic from infectious or vascular etiologies. KEY POINTS: Conus and cauda lesions have varied causes; MRI with contrast is vital for accurate diagnosis. Myxopapillary ependymomas cause vertebral scalloping; schwannomas may be cystic; intramedullary gliomas expand the cord. Conus medullaris and cauda lesions overlap clinically; imaging helps distinguish neoplastic from infectious or vascular causes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".