Intra-Rater Reliability of Rehabilitative Ultrasound Imaging for Multifidus Muscles Thickness and Cross Section Area in Healthy Subjects
Bibliographic record
Abstract
INTRODUCTION: Rehabilitative Ultrasound Imaging (RUSI) must be valuable method for research and rehabilitation. So, the reliability of its measurements must be determined. The purpose of this study was to evaluate the intra-rater reliability of RUSI for measurement of multifidus (MF) muscles cross section areas (CSAs), bladder wall diameter, and thickness of MF muscles between 2 sessions in healthy subjects. METHOD: Fifteen healthy subjects through simple non-probability sampling participated in this single-group repeated-measures reliability study. MF muscles thickness at rest and during contraction, MF muscles CSAs at rest, and bladder diameters at rest and during pelvic floor muscles (PFM) contraction were measured through RUSI. Pearson's correlation coefficient test was used to determine intra-rater reliability of variables. FINDING: The results showed that intra-class correlation Coefficient (ICCs) values with 95% confidence interval (CI) and the standard error of the measurement (SEM) were good to excellent agreement for a single investigator between measurement occasions. The intra-rater reliability for the bladder wall displacement was high (ICCs for rest and PFM contraction state: 0.96 and 0.95 respectively), for the MF muscles CSAs at the L4 level was good to high (ICCs 0.75 and 0.91 for right (Rt) and left (Lt) side respectively), and for the thickness of MF muscles at two levels, at rest and during two tasks was moderate to high (ICCs: 0.64 to 0.87). CONCLUSION: The Trans-Abdominal (TA) method of RUSI is a reliable method to quantify the PFM contraction in healthy subjects. Also, the RUSI is a reliable method to measure the MF muscles CSAs, the MF muscles thickness at rest and during functional tasks in healthy subjects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".