RELATIONSHIP BETWEEN SAGITTAL SPINAL CURVES AND BACK SURFACE PROFILES OBTAINED WITH RADIOGRAPHS
Bibliographic record
Abstract
The purpose of this article is to introduce a novel approach, by using coupled video and radiographic analysis of back surface and spinal curves in the sagittal plane to decrease the ionizing radiation exposure for subjects requiring long-term follow-up of their spinal deformity. This approach is specifically designed for the use in a clinical set-up for the follow-up of subjects with progressive spinal deformities. The subjects are radiographed with nine steel balls embedded in circular markers evenly distributed on the subject's back surface over the spinous processes of C7 to S1. A technique allows to draw the external back profile and the spinal curve. Patient-specific transfer functions are defined moving the back profile to the spinal curve. Sixteen adult volunteers were tested to validate the concepts proposed. For each of them, the values of transfer functions between radiographic back surface profile and corresponding spinal curve have been calculated. Each internal curve is correctly simulated when based upon the back profile. Our research is now focused on the prediction of the internal curve of patients from their back surface profile based on patient-specific transfer functions.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".