Bone Allograft Pedicle Screw Augmentation
Bibliographic record
Abstract
STUDY DESIGN: We performed a comprehensive cadaveric biomechanical study to compare the fixation strength of primary screws, screws augmented with bone allograft, and screws augmented with polymethylmethacrylate cement. OBJECTIVE: To evaluate a novel technique for screw augmentation using morselized cortico-cancellous bone allograft to fill the widened screw track of failed pedicle screws. BACKGROUND: To date, there are no known biological methods available for failed pedicle screw augmentation or fixation. MATERIALS AND METHODS: Biomechanical tests were performed using 2 different testing modalities to quantify fixation strength including axial screw pullout and progressive cyclic displacement tests. RESULTS: Fifty vertebrae were instrumented with pedicle screws. Our study showed that bone allograft augmentation using the same diameter screw was noninferior to the fixation strength of the initial screw. In the axial pullout test, screws undergoing bone allograft repair failed at 25% lower loads compared with native screws, and screws augmented with cement showed approximately twice as much strength compared with native screws. In the cyclic displacement test, screws fixed with cement resisted loosening the best of all the groups tested. However, screws augmented with bone graft were found to have an equal strength to native screw purchase. our study did not find a correlation with bone mineral density as a predictor for failure in axial pullout or cyclic displacement tests. CONCLUSION: Bone allograft augmentation for pedicle screw fixation was noninferior to the initial screw purchase in this biomechanical study. This bone allograft technique is a viable option for screw fixation in the revision setting when there is significant bone loss in the screw track.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".