Radiographic Assessment and Quantitative Motion Analysis of the Cervical Spine After Serial Sectioning of the Anterior Ligamentous Structures
Bibliographic record
Abstract
In Brief Study Design. Cadaveric study of a diagnostic test for cervical spine instability. Objective. Determine if flexion-extension (FE) radiographs can be used to detect incremental damage to anterior cervical structures. Summary of Background Data. Prior studies have shown that damage to cervical structures can alter motion between vertebrae, and FE radiographs are sometimes used to detect this damage. However, no study has determined if FE radiographs are sensitive and specific for acute injury. Methods. FE radiographs were taken of the intact neck and after each incremental increase in damage to the anterior structures. Intervertebral motion was quantified using previously validated methods. The sensitivity and specificity of intervertebral motion measurements were assessed. Results. Motion within the intact spines was within normal ranges. Although intervertebral rotation changed significantly after certain anterior structures were damaged, rotation frequently remained within normal ranges, even after extensive damage. A center of rotation that was posterior to the 95% confidence interval for normal motion was 100% sensitive and specific for damage to the anterior structures of the spine. Conclusions. The results suggest that extensive damage to the anterior cervical spine could be missed if instability assessment was based on intervertebral rotation or displacements measured from FE radiographs. In contrast, a center of rotation that was located posterior to normal was both sensitive and specific for damage to anterior structures. Analysis of cervical intervertebral motion in whole cadavers suggests that extensive damage may not cause rotation or shear to fall outside the normal range. In contrast, a center of rotation that is posterior to the normal range was sensitive and specific for damage to anterior structures of the cervical spine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".