An exploratory cohort study of serum estradiol, testosterone, osteoprotegerin, interleukin-6, calcium, and magnesium as potential biomarkers of cervical spondylosis
Bibliographic record
Abstract
Abstract Background Exploration of biomarkers for debilitating diseases such as cervical spondylosis is important to revolutionize clinical diagnosis and management of such conditions. The study aimed to determine the correlation between neck pain and disability and serum levels of interleukin-6 (IL-6), osteoprotegerin (OPG), estradiol (E2), testosterone (TES), calcium (Ca), and magnesium (Mg) among individuals with symptomatic cervical spondylosis. Methods This study was a cohort design. The participants were new referrals to two Nigerian physical therapy clinics. Participants’ neck pain intensity (PI), neck disability index (NDI), IL-6, OPG, E2, TES, Ca, and Mg were measured at baseline and after 13 weeks of follow-up. Data were analyzed using descriptive statistics, independent samplesttest, Pearson’s correlation, and multiple linear regression. Results Forty individuals aged 52.40 ± 8.60 years participated in the study. Women had significantly higher levels of IL-6 (t = − 2.392,p = 0.026), OPG (t = − 3.235,p = 0.005), E2 (t = − 6.841,p = 0.001), but lower TES (t = 17.776,p = 0.001). There were no significant sex differences in PI and NDI. There were significant correlations between PI and OPG (r = 0.385,p < 0.001), NDI and OPG (r = 0.402,p < 0.001), and IL-6 (r = 0.235,p = 0.036). Significant predictors of PI were OPG (β = 0.442,p < 0.001) and E2 (β = − 0.285,p = 0.011), and NDI were OPG (β = 0.453,p < 0.001), E2 (β = − 0.292,p = 0.005), and IL-6 (β = 0.225,p = 0.024). Conclusion High serum levels of IL-6 and OPG were associated with cervical spondylosis severity. However, high serum levels of E2 and TES correlated with lesser severity. Moreover, TES inversely correlated with the proinflammatory cytokines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".