The Effect of Averaging Multiple Trials on Measurement Error During Ultrasound Imaging of Transversus Abdominis and Lumbar Multifidus Muscles in Individuals With Low Back Pain
Bibliographic record
Abstract
STUDY DESIGN: Clinical measurement, reliability study. OBJECTIVES: To investigate the improvements in precision when averaging multiple measurements of percent change in muscle thickness of the transversus abdominis (TrA) and lumbar multifidus (LM) muscles. BACKGROUND: Although the reliability of TrA and LM muscle thickness measurements using rehabilitative ultrasound imaging (RUSI) is good, measurement error is often large relative to mean muscle thickness. Additionally, percent thickness change measures incorporate measurement error from both resting and contracted conditions. METHODS: Thirty volunteers with nonspecific low back pain participated. Thickness measurements of the TrA and LM muscles were obtained using RUSI at rest and during standardized tasks. Percent thickness change was calculated with the formula thickness(contracted) - thickness(rest)/thickness(rest). Standard error of measurement (SEM) quantified precision when using 1 or a mean of 2 to 6 consecutive measurements. RESULTS: Compared to when using a single measurement, SEM of both the TrA and LM decreased by nearly 25% when using a mean of 2 measures, and by 50% when using the mean of 3 measures. Little precision was gained by averaging more than 3 measurements. CONCLUSION: When using RUSI to determine percent change in TrA and LM muscle thickness, intra examiner measurement precision appears to be optimized by using an average of 3 consecutive 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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".