Inter-Rectus Distance Measurement Using Ultrasound Imaging: Does the Rater Matter?
Bibliographic record
Abstract
Purpose: To investigate the interrater reliability of inter-rectus distance (IRD) measured from ultrasound images acquired at rest and during a head-lift task in parous women and to establish the standard error of measurement (SEM) and minimal detectable change (MDC) between two raters. Methods: Two physiotherapists independently acquired ultrasound images of the anterior abdominal wall from 17 parous women and measured IRD at four locations along the linea alba: at the superior border of the umbilicus, at 3 cm and 5 cm above the superior border of the umbilicus, and at 3 cm below the inferior border of the umbilicus. The interrater reliability of the IRD measurements was determined using intra-class correlation coefficients (ICCs). Bland-Altman analyses were used to detect bias between the raters, and SEM and MDC values were established for each measurement site. Results: When the two raters performed their own image acquisition and processing, ICCs (3,5) ranged from 0.72 to 0.91 at rest and from 0.63 to 0.96 during head lift, depending on the anatomical measurement site. Bland-Altman analyses revealed no systematic bias between the raters. SEM values ranged from 0.23 cm to 0.71 cm, and MDC values ranged from 0.64 cm to 1.97 cm. Conclusion: When using ultrasound imaging to measure IRD in women, it is acceptable for different therapists to compare IRDs between patients and within patients over time if IRD is measured above or below the umbilicus. Interrater reliability of IRD measurement is poorest at the level of the superior border of the umbilicus.
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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 | 0.221 | 0.423 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".