Digital Image Correlation (DIC) Assessment of the Non-Linear Response of the Anterior Longitudinal Ligament of the Spine during Flexion and Extension
Bibliographic record
Abstract
While the non-linear behavior of spine segments has been extensively investigated in the past, the behavior of the Anterior Longitudinal Ligament (ALL) and its contribution during flexion and extension has never been studied considering the spine as a whole. The aims of the present study were to exploit Digital Image Correlation (DIC) to: (I) characterize the strain distribution on the ALL during flexion-extension, (II) compare the strain on specific regions of interest (ROI) of the ALL in front of the vertebra and of the intervertebral disc, (III) analyze the non-linear relationship between the surface strain and the imposed rotation and the resultant moment. Three specimens consisting of 6 functional spinal units (FSUs) were tested in flexion-extension. The full-field strain maps were measured on the surface of the ALL, and the most strained areas were investigated in detail. The DIC-measured strains showed different values of peak strain in correspondence with the vertebra and the disc but the average over the ROIs was of the same order of magnitude. The strain-moment curves showed a non-linear response like the moment-angle curves: in flexion the slope of the strain-moment curve was greater than in extension and with a more abrupt change of slope. To the authors' knowledge, this is the first study addressing, by means of a full-field strain measurement, the non-linear contribution of the ALL to spine biomechanics. This study was limited to only three specimens; hence the results must be taken with caution. This information could be used in the future to build more realistic numerical models of the spine.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".