The predictive value of preoperative paraspinal muscle morphometry on complications after lumbar surgery: a systematic review
Bibliographic record
Abstract
PURPOSE: The effect of paraspinal muscles atrophy and fat infiltration (FI) on the complications of spinal surgery has not been established. METHODS: A review of the literature was conducted from a search of the PubMed, EMBASE, and Web of Science databases from inception through January 2021. The literature was searched and assessed by independent reviewers based on criteria that included an assessment of preoperative paraspinal muscle morphology in addition to measuring its relationship to surgical complications. All relevant papers were assessed for risk of bias according to the modified Newcastle Ottawa Scale and the Joanna Briggs Institute Critical Appraisal Tools. A narrative synthesis was conducted. RESULTS: The initial search yielded 5632 studies, of which 16 studies were included in the analysis. All included studies were at a low risk of bias. There existed strong evidence that the atrophy and FI of paraspinal muscles had an association with the development of bone nonunion (two high quality studies), pedicle screw loosening (two high quality studies), adjacent segment degeneration (three high quality studies) and proximal junctional kyphosis (five high quality studies) after lumbar surgery. Besides, there is also limited evidence for association between atrophy and FI of paraspinal extensor muscles and less local and global curve improvement. CONCLUSIONS: Strong evidence was found for an association between preoperative paraspinal muscle degeneration and multiple postoperative complications after lumbar surgery. However, the findings should be interpreted with caution due to the small quantity of the available literature and high heterogeneity among studies.
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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 | 0.014 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".