An automated CT-based method to quantify the 3D anatomy of the posterior elements of the human thoracic and lumbar spine
Why this work is in the frame
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Bibliographic record
Abstract
This study presents the development and implementation of a highly automated computed tomography-based method to quantify 3D posterior element anatomy. The use of the posterior elements as the site of the bone–implant interface in spinal posterior fusion plating requires quantitative morphologic human data on these structures. Human thoracic and lumbar vertebrae (n = 307) were automatically segmented to isolate the spinous processes and laminae using atlas-based deformable registration. Analysis was conducted to determine lateral facing surface area, transverse and coronal plane angulation, and interspinous process distance between adjacent levels. The considered metrics demonstrated division based on spinal level (T12–L5 vs. T1–T11), with larger measured outcomes for males. This anatomic study provides an automated method for acquiring 3D morphological information that can be used to guide posterior element device design.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it