Association of neutrophil to lymphocyte ratio with bone mineral density in post-menopausal women: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background We conducted a systematic review and meta-analysis to compare the neutrophil lymphocyte ratio (NLR) levels between women with post-menopausal osteopenia or osteoporosis to those with normal bone mineral density (BMD). Methods We used Web of Science, PubMed, and Scopus to conduct a systematic search for relevant publications published before June 19, 2022, only in English language. We reported standardized mean difference (SMD) with a 95% confidence interval (CI). Because a significant level of heterogeneity was found, we used the random-effects model to calculate pooled effects. We used the Newcastle–Ottawa scale for quality assessment. Results Overall, eight articles were included in the analysis. Post-menopausal women with osteoporosis had elevated levels of NLR compared to those without osteoporosis (SMD = 1.03, 95% CI = 0.18 to 1.88, p = 0.017, I 2 = 98%). In addition, there was no difference between post-menopausal women with osteopenia and those without osteopenia in neutrophil lymphocyte ratio (NLR) levels (SMD = 0.58, 95% CI=-0.08 to 1.25, p = 0.085, I 2 = 96.8%). However, there was no difference between post-menopausal women with osteoporosis and those with osteopenia in NLR levels (SMD = 0.75, 95% CI=-0.01 to 1.51, p = 0.05, I 2 = 97.5%, random-effect model). Conclusion The results of this study point to NLR as a potential biomarker that may be easily introduced into clinical settings to help predict and prevent post-menopausal osteoporosis.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".