Quantitative Magnetic Resonance Imaging Analysis of the Cervical Spine Extensor Muscles: A Pilot Study
Bibliographic record
Abstract
Introduction Variations in cervical muscle cross-sectional area (CSA) and composition, particularly of the multifidus muscle, have been reported in patients with chronic neck pain. However, few studies have reported on the reliability of such muscle measurements and there remains no standard protocol for tissue segmentation. Therefore, the purpose of this pilot study was to provide a detailed muscle measurement protocol and determine the reliability of associated cervical muscle size and composition measurements using an open-source image analysis software (ImageJ). Material and Methods Cervical magnetic resonance images of 10 individuals with spinal stenosis were selected from an internal database. Muscle CSA and functional cross-sectional area (FCSA, fat-free area) measurements of the multifidus, semispinalis cervicis, semispinalis capitis, and splenius capitis were acquired bilaterally from axial T2-weighted magnetic resonance image from C2–C3 to C6–C7 levels. All measurements were repeated twice, at least 5 days apart and the assessor was blinded to all earlier measurements. Results The reliability for the upper (C2–C3 and C3–C4) and lower cervical levels (C4–C5, C5–C6, and C6–C7) was assessed separately. The intrarater reliability measurements were comparable between muscles and spinal levels. The intraclass correlation coefficient (ICC) for the CSA measurements varied between 0.79 to 0.97 at C2–C4 and 0.75 to 0.91 at C4–C7. The reliability was similar for the FCSA measurements and varied between 0.73 to 0.93 at C2–C4 and 0.78 to 0.90 at C4–C7. Conclusion The results of this pilot study suggest that the proposed method to investigate cervical muscle size and composition is reliable, with moderate-to-excellent reliability across cervical muscles and vertebral levels.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".