Comparison of Strain Rosettes and Digital Image Correlation for Measuring Vertebral Body Strain
Bibliographic record
Abstract
Strain gages are commonly used to measure bone strain, but only provide strain at a single location. Digital image correlation (DIC) is an optical technique that provides the displacement, and therefore strain, over an entire region of interest on the bone surface. This study compares vertebral body strains measured using strain gages and DIC. The anterior surfaces of 15 cadaveric porcine vertebrae were prepared with a strain rosette and a speckled paint pattern for DIC. The vertebrae were loaded in compression with a materials testing machine, and two high-resolution cameras were used to image the anterior surface of the bones. The mean noise levels for the strain rosette and DIC were 1 με and 24 με, respectively. Bland-Altman analysis was used to compare strain from the DIC and rosette (excluding 44% of trials with some evidence of strain rosette failure or debonding); the mean difference ± 2 standard deviations (SDs) was -108 με ± 702 με for the minimum (compressive) principal strain and -53 με ± 332 με for the maximum (tensile) principal strain. Although the DIC has higher noise, it avoids the relatively high risk we observed of strain gage debonding. These results can be used to develop guidelines for selecting a method to measure strain on bone.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".