Evaluating the ability of posterior elements to support new instrumentation for spinal fusion
Bibliographic record
Abstract
Posterior spinal plating devices have recently made a re-emergence as both stand-alone devices and for use in conjunction with anterior fusion. Yet, the structural integrity of the posterior elements to support loads throughout the spine and the impact of plating on posterior element strength has not been well characterized. This study aims to quantify the mechanical strength of the posterior elements (spinous processes/laminae) throughout the spine and to determine the effect of attaching posterior element plating systems on their ultimate load to failure. Vertebral levels from six cadaveric spines were grouped in pairs to account for varying geometries and sizes of the human posterior elements (a total of 59 levels in 5 groups). One sample from each pair was tested in its native state, and the complementary vertebra was tested via posterior plating. Posterior element plating caused moderate reductions in posterior element failure strength (15-24 percent) throughout the cervical, thoracic, and lumbar spine. Bone mineral density of the posterior elements had the most significant impact on ultimate load to failure (a decrease of 0.1 g/cm3, yields a 189N reduction in). The modest structural impact of posterior element plating motivates continued investigation into potential use of less invasive plating devices for posterior spinal fusion.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".