Characterizing the Location of Spinal and Vertebral Levels in the Human Cervical Spinal Cord
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Advanced MR imaging techniques are critical to understanding the pathophysiology of conditions involving the spinal cord. We provide a novel, quantitative solution to map vertebral and spinal cord levels accounting for anatomic variability within the human spinal cord. For the first time, we report a population distribution of the segmental anatomy of the cervical spinal cord that has direct implications for the interpretation of advanced imaging studies most often conducted across groups of subjects. MATERIALS AND METHODS: Twenty healthy volunteers underwent a T2-weighted, 3T MRI of the cervical spinal cord. Two experts marked the C3-C8 cervical nerve rootlets, C3-C7 vertebral bodies, and pontomedullary junction. A semiautomated algorithm was used to locate the centerline of the spinal cord and measure rostral-caudal distances from a fixed point in the brain stem, the pontomedullary junction, to each of the spinal rootlets and vertebral bodies. Distances to each location were compared across subjects. Six volunteers had 2 additional scans in neck flexion and extension to measure the effects of patient positioning in the scanner. RESULTS: We demonstrated that substantial variation exists in the rostral-caudal position of spinal cord segments among individuals and that prior methods of predicting spinal segments are imprecise. We also show that neck flexion or extension has little effect on the relative location of vertebral-versus-spinal levels. CONCLUSIONS: Accounting for spinal level variation is lacking in existing imaging studies. Future studies should account for this variation for accurate interpretation of the neuroanatomic origin of acquired MR signals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".