Reliability and Accuracy of Ultrasound Measurement of Hip Displacement in Children with Cerebral Palsy
Bibliographic record
Abstract
Objective Hip migration percentage (MP) measured on anteroposterior pelvis radiographs is the gold standard to assess the severity of hip displacement in children with cerebral palsy (CP). Repeated exposure of these children to ionizing radiation under a hip surveillance program is undesirable. Recently, a semi-automatic approach to measure MP US on ultrasound (US) images was validated in a phantom study. This pilot in vivo study applied the previous phantom method and aimed to determine the reliability and accuracy of the MP US . Methods Thirty-four children (23 boys and 11 girls) aged 8.9 ± 3.1 y old and diagnosed with CP were recruited. A total of 59 hips were scanned once, while 43 of these were scanned twice to evaluate the test-retest reliability. Two raters (R1 and R2) manually measured MP US ; procedures included selecting images of interest, cropping a region of interest and removing soft tissues on hip US images. Custom software was developed to measure MP automatically after the manual pre-image processing. Results The intra-class correlation coefficients (ICC 2,1 ) for the test-retest (R1), intra-rater (R1) and inter-rater (R1 vs R2) reliabilities were 0.90, 0.94 and 0.82, respectively. The standard error of measurement of MP US for all three evaluations was ≤3.0%. The mean absolute difference between MP US and MP X-ray and the percentage of MP US within clinical acceptance error of 10% for R1 and R2 were (R1: 6.2% ± 4.9%, 84.7%) and (R2: 7.6% ± 6.1%, 73.7%), respectively. Conclusion This study demonstrated that US scans were repeatable and MP US could be measured reliably and accurately.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".