Ultrasound Imaging Analysis of the Lumbar Multifidus Muscle Echo Intensity: Intra-Rater and Inter-Rater Reliability of a Novice and an Experienced Rater
Bibliographic record
Abstract
Background and Objectives: Ultrasound echo intensity (EI) of the lumbar multifidus muscle (LMM) could offer valuable insights regarding muscle quality in people with low back pain (LBP). However, whether the rater’s experience noticeably influences the reliability and precision of LMM EI measurements has not been established. The aims of this study were to investigate the intra-rater and inter-rater reliability of LMM EI measurements, and to compare the reliability and SEM between a novice and an experienced rater. Materials and Methods: Twenty athletes (10 females, 10 males) with a history of LBP were included in this study. Transverse ultrasound images of LMM were taken at L5 in prone. LMM EI measurements were obtained bilaterally by tracing the maximum ROI representing the LMM cross-sectional area (CSA), avoiding the inclusion of bone or surrounding fascia. All measurements were performed by two novice raters and an experienced researcher. Each measurement was acquired by each rater three times for each side on three different images, and the average was used in the analyses. Raters were blinded to each other’s measurements and the participant’s clinical information. Intra-class correlation coefficients (ICCs) were obtained to assess the intra-rater and inter-rater reliability. Results: The intra-rater ICC values for the LMM measurements for the experienced rater were excellent (ICC all > 0.997). The inter-rater reliability ICC values showed moderate to excellent reliability (0.614 to 0.994) and agreement between the novice raters and the experienced rater, except for Novice 1 for the right LMM, which revealed lower ICCs and a wider 95% CI. Intra-rater and inter-rater reliability results were similar when separately looking at the right and left side of the muscle and participant gender. Conclusions: Our findings support the clinical use of ultrasound imaging for the assessment of LMM EI in individuals with LBP.
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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 | 0.001 |
| 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 | 0.001 |
| 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".