Test-retest standard error of measurements for full-torso surface topography parameters in healthy teenagers
Bibliographic record
Abstract
To assess the test-retest standard error of measurement (SEM) of full-torso surface topography (ST) parameters in adolescents without spinal deformities. ST is used to quantify the external deformity of the torso due to scoliosis. A normative ST database is being developed to help interpret the ST parameters used to describe scoliosis. Test-retest SEM has not been estimated for most parameters in this population. Twenty-two healthy volunteers between 10-17 years old, with a body mass index of 19.1 ± 3.3 kg/m2, scoliometer measure of 3.6° ± 2.5°, and without pain were included. Four Minolta 910 Laser Scanners and a standard positioning frame were used to record ST scans. One evaluator positioned all subjects, marked 11 reference points, and scanned. Immediately after, reference points were erased, landmarking and scanning repeated. ST parameters were extracted with custom designed software in Matlab by one evaluator digitizing reference points. Nineteen previously published and 7 newly proposed ST parameters were extracted. Test-retest standard error of measurement was calculated for each parameter. SEM was estimated for the minimum, maximum and the range of within-subject values for parameters extracted. SEM < 4 mm, < 5° or < 0.2 for a ratio were considered adequate based on values in patients with scoliosis. (Figure 1 ) Figure 1 Test-retest SEM of 15 of 26 ST parameters in healthy adolescents were found adequate for developing a normative database. Six of the 7 newly developed parameters had adequate SEM.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".