A Review of Strategies to Improve Biomechanical Fixation in the Cervical Spine
Bibliographic record
Abstract
STUDY DESIGN: Systematic review. OBJECTIVES: Review the surgical techniques and construct options aimed at improving the biomechanical strength of cervical constructs. METHODS: A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search of the MEDLINE, Embase, and Cochrane Library databases was performed to identify all studies examining biomechanical strategies utilized in the osteoporotic cervical spine. Screening was performed in duplicate for all stages of the review process. RESULTS: An initial search returned 3887 articles. After deletion of duplications and review of abstracts and full text, 39 articles met inclusion criteria. Overall, the surgical techniques reviewed aimed at obtaining rigid fixation in the setting of poor bone quality, or dispersing the forces at the bone-implant interface. We identified 6 key techniques to improve biomechanical fixation. These include bicortical fixation, appropriate screw selection (size and trajectory), PMMA augmentation, load sharing techniques, consideration of ancillary fixation around the occipitocervical junction, and supplementing the construct with post-operative collar or halo. CONCLUSION: The summation of the literature highlights a framework of modalities available to surgeons to improve biomechanical fixation in the cervical spine. While these may improve construct strength in the setting of osteoporosis, there is a paucity of evidence available to make recommendations in this patient population.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".