Congenital Cervical Spine Stenosis in a Multicenter Global Cohort of Patients With Degenerative Cervical Myelopathy: An Ambispective Report Based on a Magnetic Resonance Imaging Diagnostic Criterion
Bibliographic record
Abstract
BACKGROUND: Congenital spinal stenosis (CSS) of the cervical spine is a risk factor for acute spinal cord injury and development of degenerative cervical myelopathy (DCM). OBJECTIVE: To develop magnetic resonance imaging (MRI)-based criteria to diagnose preexisting CSS and evaluate differences between patients with and without CSS. METHODS: A secondary analysis of international prospectively collected data between 2005 and 2011 was conducted. We examined the data of 349 surgical DCM patients and 27 controls. Spinal canal and cord anteroposterior diameters were measured at noncompressed sites to calculate spinal cord occupation ratio (SCOR). Torg-Pavlov ratios and spinal canal diameters from radiographs were correlated with SCOR. Clinical and MRI factors were compared between patients with and without CSS. Surgical outcomes were also assessed. RESULTS: Calculation of SCOR was feasible in 311/349 patients. Twenty-six patients with CSS were identified (8.4%). Patients with CSS were younger than patients without CSS (P = .03) and had worse baseline severity as measured by the modified Japanese Orthopedic Association score (P = .04), Nurick scale (P = .05), and Neck Disability Index (P < .01). CSS patients more commonly had T2 cord hyperintensity changes (P = .09, ns) and worse SF-36 Physical Component scores (P = .06, ns). SCOR correlated better with Torg-Pavlov ratio and spinal canal diameter at C3 than C5. Patients with SCOR ≥ 65% were also younger but did not differ in baseline severity. CONCLUSION: SCOR ≥ 70% is an effective criterion to diagnose CSS. CSS patients develop myelopathy at a younger age and have greater impairment and disability than other patients with DCM. Despite this, CSS patients have comparable duration of symptoms, MRI presentations, and surgical outcomes to DCM patients without CSS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".