The effect of disc degeneration on anterior shear translation in the lumbar spine
Bibliographic record
Abstract
Many pathologies involving disc degeneration are treated with surgery and spinal implants. It is important to understand how the spine behaves mechanically as a function of disc degeneration. Shear loading is especially relevant in the natural and surgically stabilized lumbar spine. The objective of our study was to determine the effect of disc degeneration on anterior translation of the lumbar spine under shear loading. We tested 30 human cadaveric functional spinal units (L3-4 and L4-5) in anterior shear loading. First, the specimens were imaged in a 1.5 T magnetic resonance scanner. The discs were graded according to the Pfirrmann classification. The specimens were then loaded up to 250 N in anterior shear with an axial compression force of 300 N. Motion of the vertebrae was captured with an optoelectronic camera system. Inter- and intra-observer reliability for disc grading was determined (Cohen's and Fleiss' Kappa), and a non-parametric test was performed on the translation data to characterize the effect of disc degeneration on this parameter. We found fair to moderate agreement between and within observers for the disc grading. We found no significant effect of disc degeneration on anterior shear translation (Kruskal-Wallis ANOVA). Our results indicate that disc degeneration, as classified with the Pfirrmann scale, does not predict lumbar spinal motion in shear.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".