Reliability of 2 Ultrasonic Imaging Analysis Methods in Quantifying Lumbar Multifidus Thickness
Bibliographic record
Abstract
STUDY DESIGN: Reliability study. OBJECTIVES: To compare the within- and between-day intrarater reliability of rehabilitative ultrasound imaging (RUSI) using static images (static RUSI) and video clips (video RUSI) to quantify multifidus muscle thickness at rest and while contracted. Secondary objectives were to compare the measurement precision of averaging multiple measures and to estimate reliability in individuals with and without low back pain (LBP). BACKGROUND: Although intrarater reliability of static RUSI in measuring multifidus thickness has been established, using video RUSI may improve reliability estimates, as it allows examiners to select the optimal image from a video clip. Further, multiple measurements and LBP status may affect RUSI reliability estimates. METHODS: Static RUSI and video RUSI were used to quantify multifidus muscle thickness at rest and during contraction and percent thickness change in 27 volunteers (13 without LBP and 14 with LBP). Three static RUSI images and 3 video RUSI video clips were collected in each of 2 sessions 1 to 4 days apart. Reliability and precision were assessed using intraclass correlation coefficients, standard error of measurement, minimal detectable change, bias, and 95% limits of agreement. RESULTS: Using an average of 2 measures yielded optimal measurement precision for static RUSI and video RUSI. Based on the average of 2 measures obtained under the same circumstance, there was no significant difference in the reliability estimates between static RUSI and video RUSI across all testing conditions. Reliability point estimates (intraclass correlation coefficient model 3,2) of multifidus thickness were 0.99 for within-day comparisons and ranged from 0.93 to 0.98 for between-day comparisons. The within- and between-day intraclass correlation coefficients (model 3,2) of percent thickness change ranged from 0.97 to 0.99 and from 0.80 to 0.90, respectively. The exploratory analysis showed no significant difference in the reliability estimates between asymptomatic and LBP participants across most testing conditions. CONCLUSION: Both RUSI methods yielded high reliability estimates for multifidus muscle measurements. Using an average of 2 measures obtained optimal measurement precision. Overall, video RUSI is a reliable surrogate for static RUSI for multifidus muscle measurements and has the additional advantage of requiring shorter data collection time.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".