In Vivo Quantitative Analysis of Scoliotic Vertebrae
Bibliographic record
Abstract
An in vivo method based on CT images and finite element meshing had been developed to quantify and visualize the bone density distribution of scoliotic vertebrae. CT examination (axial acquisition of the apical, superior and inferior adjacent vertebral bodies) had been performed on seven girls presenting an idiopathic scoliosis. Using an in-house image processing software and the pre-post processor Patran, a surfacic finite element mesh of each body slice was proposed allowing an automatic mapping of the cancellous bone slices and a volumic mesh for the bone density distribution visualization. In the coronal plane, compared to the body geometrical centre, the body mechanical centre was shifted forward in the concavity of the curvature for six patients and in the convexity for one patient. For each patient, this shift forward was made in a same way for the three vertebrae. In the sagittal plane, the body mechanical inertia centre was shifted forward in the posterior side for 12 vertebrae, in the anterior side for 3 vertebrae and was not shifted forward for 6 vertebrae. This shift forward was made in the anterior side for the inferior adjacent vertebra. The shift forward by slice was made in a same way for each slice, excepted at the end plates. Besides, one can observe that the scoliotic deformation evolution seemed to modify the mechanical property distribution. The results may also suggest predictive criteria of evolution of the scoliotic deformities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".