2015 Young Investigator Award Winner
Bibliographic record
Abstract
STUDY DESIGN: A cross-sectional cadaveric examination of the mechanical effect of foraminal ligaments on cervical nerve root displacement and strain. OBJECTIVE: To determine the role of foraminal ligaments by examining differences in cervical nerve root displacement and strain during upper limb neural tension testing (ULNTT) before and after selective cutting of foraminal ligaments. SUMMARY OF BACKGROUND DATA: Although investigators have determined that lumbar spine foraminal ligaments limit displacement and strain of lumbosacral nerve roots, similar studies have not been conducted to prove that it is true for the cervical region. Because the size, shape, and orientation of cervical spine foraminal ligaments are similar to those in the lumbar spine, it is hypothesized that foraminal ligaments in the cervical spine will function in a similar fashion. METHODS: Radiolucent markers were implanted into cervical nerve roots C5-C8 of 9 unembalmed cadavers. Posteroanterior fluoroscopic images were captured at resting and upper limb neural tension testing positioning before and after selective cutting of foraminal ligaments. RESULTS: Selective cutting of foraminal ligaments resulted in significant increases in inferolateral displacement (average, 2.94 mm [ligaments intact]-3.87 mm [ligaments cut], P < 0.05) and strain (average, 9.33% [ligaments intact]-16.31% [ligaments cut], P < 0.03) of cervical nerve roots C5-C8 during upper limb neural tension testing. CONCLUSION: Foraminal ligaments in the cervical spine limited cervical nerve root displacement and strain during upper limb neural tension testing. Foraminal ligaments seem to have a protective role, reducing displacement and strain to cervical nerve roots during tension events. LEVEL OF EVIDENCE: 2.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.526 | 0.345 |
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".