Identifying the Best Surface Topography Parameters for Detecting Idiopathic Scoliosis Curve Progression
Bibliographic record
Abstract
There is no consensus on which surface topography (ST) parameters may be used to detect scoliosis progression. The sensitivity to change of common ST parameters has not yet been compared. The goal of this study was to determine which ST parameters are most sensitive to scoliosis progression in patients with adolescent idiopathic scoliosis (AIS) receiving conservative treatment. Fifty-eight subjects with AIS were included whose Cobb angle had progressed by at least 5 degrees during a 1 year interval. All had had ST scans and frontal radiographs at a 12 month interval at our clinic. Commonly used back-only ST parameters and contributing scores were derived by one evaluator. Standardized response mean (SRM) and 95% confidence intervals (CI) were calculated using the absolute value of the changes between baseline and follow-up to reflect change in deformity, independent of direction. Decompensation, cosmetic score, Deformity in the Axial Plane Index (DAPI), trunk rotation, Hump Sum, and lordosis angle were highly sensitive to scoliosis progression (SRM>0.8). Cosmetic score, Posterior Trunk Symmetry Index (POTSI), and kyphosis angle had significantly poorer SRM values than the Cobb angle. All other ST parameters had SRM estimates that did not differ significantly from the Cobb angle, suggesting that they have a similar ability to detect progression The ST measures that were most sensitive to detection of scoliosis progression in the frontal, transverse, and sagittal planes were decompensation, trunk rotation, and lordosis angle, respectively. Absolute changes in surface parameters representing either worsening or improvement externally could reflect worsening of the internal deformity. The majority of ST parameters are potentially sensitive to scoliosis progression.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".