Effect of Screw Thread Pitch, Purchase Depth, and Trajectory on Cervical Lateral Mass Fixation Strength
Bibliographic record
Abstract
STUDY DESIGN: Biomechanical study. OBJECTIVE: Determine the effect of screw thread pitch, trajectory, and purchase depth on the screw pull-out strength in lateral mass fixation constructs. SUMMARY OF BACKGROUND DATA: Fusion of the cervical spine is routinely performed with lateral mass screw fixation. It is imperative to optimize the lateral mass fixation construct strength to minimize the risk of hardware failure and subsequent complications and reoperations. METHODS: Biomechanical testing was performed using bicortical artificial bone models to replicate the lateral mass of the cervical spine. Cortical and cancellous screws of 3.5 mm diameter were compared at 3 purchase depths: unicortical, bicortical, and bicortical backed-out to unicortical, and 2 trajectories: Roy-Camille (RC) and Magerl. In a second construct, bicortical 3.5 mm screws were replaced with unicortical 4.0 mm screws. Both thread pitches and trajectories were also compared. RESULTS: Fixation with the RC technique was stronger than with Magerl in all constructs. Roy-Camille fixation using cortical screws was stronger than cancellous screws in all purchase depths. Magerl fixation using cancellous screws was stronger in all purchase depths but only statistically significant for the group where the bicortical screw was backed-out to unicortical.The construct where the bicortical 3.5 mm screw was replaced with a 4.0 mm unicortical screw was stronger than when the screw was backed-out to a unicortical depth. This was significant for cortical screws in both trajectories but only significant for cancellous screws using the RC technique. CONCLUSIONS: Biomechanical strength of cervical lateral mass fixation was shown to be directly influenced by thread pitch, depth, and trajectory. Thus, spine surgeons should be cognizant of their fixation constructs and any changes made to their components.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".