Relationship Between Paraspinal Muscle Degeneration and Functional Outcomes Following Anterior Cervical Spine Surgery for Degenerative Disk Disease: A Systematic Review
Bibliographic record
Abstract
Background/Objectives: Paraspinal muscles are important for maintaining cervical spine function and stability; however, the degeneration of these muscles is common in patients with degenerative disk disease. Such muscular changes may affect recovery trajectories and long-term functional outcomes after cervical spine surgery. This systematic review explores the existing literature on the relationship between the degree of paraspinal muscle degradation and functional outcomes following anterior cervical spine surgery in patients with cervical degenerative disk disease. Methods: A systematic review of the MEDLINE/Pubmed, Web of Science, and Embase databases was conducted according to the PRISMA guidelines up to June 2025. The inclusion criteria were patients who underwent surgery for cervical degenerative disk disease and assessments of the paraspinal muscles with magnetic resonance imaging. The methodological quality of the included studies was assessed using the Modified Newcastle–Ottawa Scale. Results: Following deduplication, a total of 3643 articles were screened, of which 6 met the inclusion criteria and were included in the review. Across these studies, a total of 515 patients were followed for at least one year. Two studies reported a negative association between paraspinal muscle degeneration and functional outcomes, three reported no association, and one reported a positive association. Conclusions: The available evidence on this topic is inconclusive. These mixed results highlight the need for further well-designed, adequately powered studies to clarify the relationship between paraspinal muscle degeneration and functional outcomes.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".