Canonical Correlations between Body Postural Variables in the Sagittal Plane and Scoliotic Variables in School-Children
Bibliographic record
Abstract
The aim of the study was analysis of the canonical correlations between body posture variables in the sagittal plane and scoliotic variables among school-children. The study included 28 girls aged 7-18. The Moiré photogrammetric method was used in the research. On the basis of the value of spine curvature angle, scoliotic posture: 1-9°; and scoliosis: ≥10° were distinguished. There were 21 (75%) with scoliotic posture and 7 (25%) with scoliosis. In the canonical correlation regarding body posture variables in the sagittal plane, the largest shares concerned: trunk inclination angle (0.035), alpha angle (0.072), angle of chest kyphosis (0.383), length of lumbar lordosis-(-0.301), actual angle of lumbar lordosis/total spine length (-1.067). In the canonical correlation regarding scoliotic variables, the largest shares were related to: shoulder asymmetry – right higher (-0.577), shoulder blade asymmetry – left higher (0.202), absolute pelvis tilt angle (-0.811), coefficient of shoulder asymmetry relative to C7 (0.324), depth of primary curvature/total spine length (0.420), primary curvature angle (0.032), length of secondary curvature/total spine length (-0.003). The high value of the canonical correlation coefficient despite lack of significance (R=0.72963; p=0.40075) indicates the possibility of the occurrence of a strong correlation of both sets of variables that can be demonstrated with a larger sample size. In the selection of scoliosis treatment method, the size of the postural variables in the sagittal plane should be taken into account, and each patient’s case should be individually considered.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".