Relative contributions of the nervous system, spinal tissue and psychosocial health to non‐specific low back pain: Multivariate meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Nervous system, psychosocial and spinal tissue biomarkers are associated with non-specific low back pain (nsLBP), though relative contributions are unclear. DATABASES AND DATA TREATMENT: MEDLINE, EMBASE, CINAHL, PsycINFO and SPORTDiscus were searched up to 25 March 2020. Related reviews and reference lists were also screened. Observational studies examining structural and functional nervous system biomarkers (e.g. quantitative sensory tests, structural and functional brain measures), psychosocial factors (e.g. mental health, catastrophizing) and structural spinal imaging biomarkers (e.g. intervertebral disc degeneration, paraspinal muscle size) between nsLBP and pain-free controls were included. For multivariate meta-analysis, two of three domains were required in each study. Random-effects pairwise and multivariate meta-analyses were performed. GRADE approach assessed evidence certainty. Newcastle-Ottawa scale assessed risk of bias. Main outcomes were the effect size difference of domains between nsLBP and pain-free controls. RESULTS: Of 4519 unique records identified, 33 studies (LBP = 1552, referents = 1322) were meta-analysed. Psychosocial state (Hedges' g [95%CI]: 0.90 [0.69-1.10], p < 0.001) in nsLBP showed larger effect sizes than nervous system (0.31 [0.13-0.49], p < 0.001; difference: 0.61 [0.36-0.86], p < 0.001) and spine imaging biomarkers (0.55 [0.37-0.73], p < 0.001; difference: 0.36 [0.04-0.67], p = 0.027). The relationship between domains changes depending on if pain duration is acute or chronic. CONCLUSIONS: Psychosocial effect sizes in nsLBP are greater than those for spinal imaging and nervous system biomarkers. Limitations include cross-sectional design of studies included and inference of causality. Future research should investigate the clinical relevance of these effect size differences in relation to pain intensity and disability. STUDY REGISTRATION: PROSPERO-CRD42020159188. SIGNIFICANCE: Spinal structural lesions (e.g. intervertebral disc degeneration), psychosocial (e.g. depression) and nervous system factors (detected by e.g. quantitative sensory tests, structural and functional measures) contribute to non-specific low back pain. However, psychosocial factors may be more compromised than nervous system and spinal imaging biomarkers. This relationship depends on if the pain is acute or chronic. These findings underscore that the 'non-specific' label in back pain should be reconsidered, and more specific multidimensional categories evaluated to guide patient management.
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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 | 0.027 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.074 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".