STIFFNESS OF OCCIPITAL-CERVICAL CONSTRUCTS
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to show that stiffness of an occipital-cervical construct can be predicted based on rod geometry and material. MATERIALS AND METHODS: Various rod-plate implants were tested as previously reported biomechanical studies of occipital-cervical fixation with the exception that no spine was used. A testing frame that holds paired contoured rods and plates to the same position as in the biomechanical testing protocol for occipital-cervical fixation was tested in the flexion-extension direction on a servo-hydraulic testing machine. Stiffness was determined from the plots of applied moment versus angular displacement. The occipital-cervical constructs were then modeled as a curved beam in pure bending in the sagittal plane to calculate the moment of inertia and theoretical stiffness. The Pearson correlation coefficient was used to assess the correlation of the experimental to the theoretical calculated stiffness. Product of inertia and material stiffness were determined for implants from previously published studies and the predicted rank order of this product was compared with the rank order of the observed biomechanical results in each study. RESULTS: A strong correlation was observed between the experimental and theoretical stiffness (R = 0.85). A strong influence of the inertia was also found on the experimental construct stiffness (R = 0.77). In five of six previously published studies, the best experimental performance was predicted using simple mechanical calculations. CONCLUSION: This study shows that both the theoretical stiffness and the calculated area moment of inertia are strongly correlated with the experimental stiffness of tested occipital-cervical fixation constructs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".