Reliability of assessing the coronal curvature of children with scoliosis by using ultrasound images
Bibliographic record
Abstract
PURPOSE: To investigate the intra- and inter-observer reliability of the coronal curvature asymmetry of children with adolescent idiopathic scoliosis (AIS) using the center of lamina (COL) method on ultrasound (US) images. METHODS: A cadaver spinal column phantom which was manipulated to present 30 scoliotic curves of varying severity of scoliotic deformities was scanned using both the US and laser scanner (LS) systems. Three observers of varying experience and skill measured the coronal curvature using the Cobb method on the LS images and the COL method on the US images. All of the measurements were performed twice, with a 1-week interval to reduce memory bias. The intra-class correlation coefficient (ICC), the mean absolute differences (MAD), and the error index (EI) were calculated to determine the agreement on selecting the end vertebrae. In addition, five AIS subjects were scanned using the US system. One observer measured the coronal curvature on the US images twice, and the measurements were compared with the Cobb angle reported in the clinical records. RESULTS: In the phantom study, the COL method showed high intra- and inter-observer reliabilities, with all ICC values >0.88. The maximum MAD of the COL measurements between different sessions among all observers was <4.1°. The EI values of the US method had similar end-vertebra selections as the LS method. The results of the pilot study showed a high intra-reliability for the US measurements. The measured difference between the Cobb and COL methods was 0.7° ± 0.5°. CONCLUSIONS: The COL method using US images appears to be a very reliable method for measuring the coronal curvature in AIS without the need to expose the patient to radiation.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".