Estimating the Compressive Strength of the Porcine Cervical Spine
Bibliographic record
Abstract
In Brief Study Design. The failure strength of porcine spinal units was correlated with vertebral size and bone mineralization. The accuracy of the resulting predictive equations was tested with an independent sample of spinal units. Objectives. To determine if dual energy x-ray absorptiometry (DXA)-obtained measures of bone mineralization can be used to accurately predict the compressive tolerance of porcine spinal units. Summary of Background Data. Porcine spinal units are often used in place of cadaveric tissues, and normalization is used to improve the transferability of model results. In compressive testing, normalization can be performed to the estimated compressive strength. Bone mineralization measures have been shown to be positively correlated with compressive tolerance and have been used to predict the tolerance of human spinal units. However, the accuracy of these predictive equations has not been assessed with an independent sample. Methods. Twenty porcine cervical spinal units were scanned (DXA) to obtain measures of bone mineral content (BMC) and bone mineral density (BMD). The units were compressed to failure, and the failure loads were correlated with the measured bone mineralization and endplate area of the spinal unit. The regression equations were used to predict the compressive tolerance of an independent sample of spinal units. Results. BMC (P = 0.078) and BMD (P = 0.2834) were not significantly correlated to compressive strength. Endplate area was the most highly correlated variable, with an r2 of 0.5329. The use of a predictive equation including BMC on the second independent sample resulted in errors of estimation of 1.4 ± 1.2 kN, corresponding to 13% of the average compressive strength. In comparison, the use of an equation employing endplate area alone resulted in estimation errors of 11%. Conclusions. Measures of BMC/BMD did not enhance predictions of compressive strength and will not reduce errors in compressive load normalization in a porcine model. The poor correlations found between BMC and compressive strength may be due to the non–load-bearing anterior processes of the porcine cervical spine. This study was performed to determine if dual energy x-ray absorptiometry-obtained measures of bone mineralization can be used to accurately predict the compressive tolerance of porcine spinal units. Endplate area (r = 0.73) was significantly correlated to compressive strength, whereas bone mineral content (r = 0.28) and bone mineral density (r = 0.18) were poorly correlated. All of the examined methods resulted in estimation errors of 10% or greater, which should be considered if these estimates are used in load normalization procedures.
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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) | 0.001 | 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 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".