The Role of Cervical Muscles Morphology in the Surgical Treatment of Degenerative Disc Disease: Clinical Correlations Based on Magnetic Resonance Imaging Studies
Bibliographic record
Abstract
Cervical spine musculature still remains a less studied component of the cervical spine anatomical compartments, although it plays a significant role in the mobility of the head and the preservation of cervical spine alignment. The goal of this study was to extract any significant information from the literature regarding the role of cervical spine muscles morphology in the outcome of surgically treated patients for degenerative disc disease (DDD) based on preoperative magnetic resonance imaging (MRI) studies. Eleven clinical case series were found, from which four were prospective and seven were retrospective. Six studies were concentrated on anterior approaches and five studies on posterior approaches in the cervical spine. In posterior approaches aiming at the preservation of muscles attachments and overall less surgical manipulations, results on cervical lordosis, axial pain and patient's functionality were found superior to traditional laminectomies. The study of cross-sectional areas (CSAs) of deep paraspinal muscles in the cervical spine could add significant information for the spine surgeon such as the prediction of adjacent level disease (ALD), fusion failure, axial pain persistence, postoperative cervical alignment and patient's postoperative functionality. It seems that MRI studies focusing on muscle layers of the cervical spine could add significant information for the spinal surgeon regarding the final surgical outcome in terms of pain and function expression. Larger multicenter clinical studies are a necessity in defining the role of the muscle component of the cervical spine in the surgical treatment of DDD.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".