Range data pre-processing for the evaluation of torso shape and symmetry in scoliosis
Bibliographic record
Abstract
Pre-processing range scans of the human torso for evaluating shape and symmetry changes in scoliosis are non-trivial. First, stray points from surrounding artefacts are often arbitrarily positioned and not amenable to automatic removal. Second, the asymmetrical alignment of the arms and neck makes cropping them difficult. Third, despite a plethora of methods, removal of holes by surface approximation for this niche application remains a challenge particularly in obscure regions like the sides and armpits. This paper proposes a novel surface approximation method and incorporates it into an integrated procedure for pre-processing range scans of the torso that includes interactive tools for cropping stray points and extremities. The new method, spline-fitted moving least squares (MLS), makes use of the Bezier curve and MLS algorithms. Numeric and clinical tests on scans of 30 volunteers, with and without scoliosis, show that the proposed method outperforms its constituent methods and a commercially available graphics package for this application.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".