Exploration of the Inter-Relationships Between Obesity, Physical Inactivity, Inflammation, and Low Back Pain
Bibliographic record
Abstract
STUDY DESIGN: Retrospective analysis wherein 103 patients were considered, and 76 patients were included: 49 were classified as chronic non-specific low back pain (CNSLBP) (Study group) and 27 had identifiable cases of specific chronic low back pain (LBP) (Control group). OBJECTIVE: Elucidate markers of systemic inflammation in patients with CNSLBP. SUMMARY OF BACKGROUND DATA: Mechanisms of LBP are poorly understood. Pro-inflammatory cytokines are increased in obesity and involved with pain modulation; we previously proposed a theoretical model of their mediating role in LBP. METHODS: Demographic information was acquired via questionnaire, chart review, and blood test data. Univariate analysis identified factors associated with CNSLBP and markers of systemic inflammation. A receiver operating curve and Youden Index were used to select optimal cut-off points for elevated C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR). Multivariable logistic regression analysis calculated the adjusted strength of relationship between factors that were proposed in our theoretical model for CNSLBP. RESULTS: Unadjusted CRP was significantly correlated with ESR (R = 0.63, P < 0.0001) and body mass index (BMI) (R = 0.38, P = 0.0015). Physically inactive patients had significantly higher CRP (6.1 vs. 1.2, P = 0.0050). ESR was significantly correlated with number of comorbidities (R = 0.34, P = 0.0047), BMI (R = 0.38, P = 0.0014), and age (R = 0.36, P = 0.0026). Physically inactive patients (10.4 vs. 3.6, P = 0.0001) and females (11.2 vs. 6.4, P = 0.0422) had significantly higher ESR. Adjusted analyses indicated significant relationships between physical inactivity and markers of systemic inflammation (adjusted odds ratios for ESR and CRP: 15.9, P = 0.0380; 15.2, P = 0.0272, respectively), and between elevated CRP and CNSLBP (adjusted odds ratio: 8.0, P = 0.0126). CONCLUSION: Systemic inflammation may act as a mediator for physical inactivity and obesity in the pathogenesis of CNSLBP. LEVEL OF EVIDENCE: 2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".