Qualitative and quantitative characteristics of the lumbar multifidi muscles: Comparison of the magnetic resonance imaging and musculoskeletal ultrasound
Bibliographic record
Abstract
Background: Paraspinal muscle pathology is often accompanied by skeletal abnormalities and is frequently associated with low back pain. While magnetic resonance imaging (MRI) can accurately assess muscle atrophy, the utility of musculoskeletal ultrasound remains under evaluation. A direct comparison between these imaging modalities has not been conducted. Objective: To compare musculoskeletal ultrasound and MRI in evaluating fatty atrophy and cross-sectional area in patients with chronic low back pain and to assess their correlations with clinical symptoms. Methods: The degree of fatty atrophy and cross-sectional area were measured using ultrasound at symptomatic and control levels in patients with chronic low back pain. A prone instability test was also performed. Ultrasound findings were compared with recent lumbar MRI results. Fatty atrophy was graded using the Kjaer system, and cross-sectional area was measured. Interobserver agreement and correlation with the available imaging were calculated. Results: Strong agreement was observed with MRI for the degree of atrophy at the symptomatic level (weighted Kappa = 0.83), but only fair agreement at the control level. Cross-sectional measurements showed poor correlation between the imaging studies at both levels (Rho = 0.03-0.07). The prone instability test was negative for all participants. Conclusion: Ultrasound reliably assesses fatty atrophy at symptomatic levels but is less accurate for circumferential measurements.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".