Localized strain measurements of the intervertebral disc annulus during biaxial tensile testing
Bibliographic record
Abstract
Both inter-lamellar and intra-lamellar failures of the annulus have been described as potential modes of disc herniation. Attempts to characterize initial lamellar failure of the annulus have involved tensile testing of small tissue samples. The purpose of this study was to evaluate a method of measuring local surface strains through image analysis of a tensile test conducted on an isolated sample of annular tissue in order to enhance future studies of intervertebral disc failure. An annulus tissue sample was biaxial strained to 10%. High-resolution images captured the tissue surface throughout testing. Three test conditions were evaluated: submerged, non-submerged and marker. Surface strains were calculated for the two non-marker conditions based on motion of virtual tracking points. Tracking algorithm parameters (grid resolution and template size) were varied to determine the effect on estimated strains. Accuracy of point tracking was assessed through a comparison of the non-marker conditions to a condition involving markers placed on tissue surface. Grid resolution had a larger effect on local strain than template size. Average local strain error ranged from 3% to 9.25% and 0.1% to 2.0%, for the non-submerged and submerged conditions, respectively. Local strain estimation has a relatively high potential for error. Submerging the tissue provided superior strain estimates.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".