Improvement on the Accuracy and Reliability of Ultrasound Coronal Curvature Measurement on Adolescent Idiopathic Scoliosis With the Aid of Previous Radiographs
Bibliographic record
Abstract
STUDY DESIGN: Retrospective study of the coronal curvature measurement on ultrasound (US) images with the aid of previous radiographs. OBJECTIVE: To compare the reliability and accuracy of the coronal curvature measurements from US images on children who have adolescent idiopathic scoliosis (AIS) with and without the knowledge of previous radiographs. SUMMARY OF BACKGROUND DATA: Using US imaging technique to measure coronal curvature on children with AIS has demonstrated high intra- and interrater reliabilities. However, the selection of end-vertebrae and the measurement difference between radiography and the US method were only moderately reliable. METHODS: Two raters measured the coronal curvatures from 65 AIS standing US spine images, without (measured one time) and with the aid of previous standing radiographs (measured two times). The intra- and interrater reliability, the correlation and the difference between the radiographic and US measurements, and the error index of the end-vertebrae selection were assessed. RESULTS: Overall, 109 curves were investigated. The intraclass correlation coefficients (ICC) of intra- and interrater reliability of the US coronal curvature measurement with the aid of previous radiographs (AOR) were 0.95 and 0.91, respectively. In comparison with the radiographic measurements, the correlation of AOR method (R) was 0.90 and the MAD was 2.8°; the corresponding results of the US measurement without the AOR (blinded US method) were 0.73° and 4.8°, respectively. The average error index on end-vertebral selection improved 43% with the AOR. CONCLUSION: The AOR method significantly improved reliability and accuracy of the spinal curvature measurement on US images compared with the blinded US method (P<0.001). It indicates that US standing images with the AOR can be used as a reliable and accurate nonionizing imaging method to monitor children with AIS. LEVEL OF EVIDENCE: 3.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".