2015 Young Investigator Award Winner
Bibliographic record
Abstract
STUDY DESIGN: A cross-sectional cadaveric examination of displacement and strain measured at the level of the cervical nerve roots during upper limb neural tension testing (ULNTT) with median nerve bias. OBJECTIVE: To determine the displacement and strain of cervical nerve roots C5-C8 during ULNTT with minimal disruption of surrounding tissues. SUMMARY OF BACKGROUND DATA: Clinical examination of neural pathology involving cervical nerve roots is difficult because of the transient nature of pathologies, such as cervical radiculopathy, entrapment neuropathies, and thoracic outlet syndrome. Cadaveric studies have demonstrated significant displacement and strain in lumbosacral nerve roots during neurodynamic testing of the lower extremity. Examination into the biomechanical behaviors of cervical nerve roots during ULNTT has not been performed. METHODS: Eleven unembalmed cadavers were positioned supine as though undergoing ULNTT. Radiolucent markers were implanted into cervical nerve roots C5-C8. Posteroanterior fluoroscopic images were captured at resting and ULNTT positioning. Images were digitized and displacement and strain were calculated. RESULTS: ULNTT resulted in significant inferolateral displacement (average, 2.16 mm-4.32 mm, P < 0.001) of cervical nerve roots C5-C8. There was a significant difference in inferolateral displacement between the C5 and C6 nerve roots (3.15 mm vs. 4.32 mm, P = 0.009). ULNTT resulted in significant strain (average, 6.80%-11.87%, P < 0.001) of cervical nerve roots C5-C8. There was a significant difference in strain between the C5 and C6 nerve roots (6.60% vs. 11.87%, P = 0.03). CONCLUSION: ULNTT caused significant inferolateral displacement and strain in cervical nerve roots C5-C8. These results provide the mechanical foundation for the use of ULNTT in clinical evaluation of pathology in the cervical region, such as in cervical radiculopathy, entrapment neuropathies, and thoracic outlet syndrome. 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) | 0.503 | 0.323 |
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".