Visual and quantitative assessment of lateral lumbar spinal canal stenosis with magnetic resonance imaging
Bibliographic record
Abstract
BACKGROUND: Lateral lumbar spinal canal stenosis is a common etiology of lumbar radicular symptoms. Quantitative measurements have commonly demonstrated better repeatability than visual assessments. We are not aware of any studies examining the repeatability of quantitative assessment of the lateral canal. PURPOSE: To evaluate the repeatability of visual assessments and newly developed quantitative measurements of lateral lumbar spinal canal stenosis using magnetic resonance imaging (MRI). MATERIAL AND METHODS: Twenty-eight patients with lateral lumbar spinal canal stenosis or prior spinal surgery with recurrent symptoms were imaged with MRI. A radiologist, a neurosurgeon and a spine research trainee graded visually and quantitatively subarticular (n = 188) and foraminal zones (n = 260) of the lateral spinal canal. Quantitative measurements included the minimal subarticular width and the cross-sectional area of the foramen. RESULTS: The repeatability of visual assessment at the subarticular zone and foraminal zones between raters varied from 0.45-0.59 and 0.42-0.53, respectively. Similarly, the intraclass correlation coefficients for the quantitative measurements varied from 0.67-0.71 and 0.66-0.76, respectively. The intra-rater repeatability for the visual assessments of the subarticular and foraminal zones was 0.70 and 0.62, respectively, while the corresponding intraclass correlation coefficients for quantitative measurements were 0.83 and 0.81, respectively. CONCLUSION: Inter-rater repeatability of visual assessments of lateral stenosis is moderate, whereas quantitative measurements of both subarticular width and the cross-sectional area of the foramen have substantial reproducibility and may be particularly useful for longitudinal studies and research purposes. The clinical value of these parameters requires further study.
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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 | 0.007 | 0.034 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".