Association between Serum Magnesium and Fractures: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
Magnesium, an essential cation for numerous cellular processes, is a major component of bone. However, its relationship with the risk of fractures is still uncertain. The present systematic review and meta-analysis aim to investigate the impact of serum Mg on the risk of incident fractures. A systematic search was conducted using several databases including PubMed/Medline and Scopus from inception to 24 May 2022, including observational studies investigating serum Mg and the incidence of fractures considered as outcomes. Abstract and full-text screenings, data extractions, and risk of bias assessments were conducted by two investigators independently. Any inconsistencies were resolved by consensus with a third author. The Newcastle–Ottawa Scale was used to assess the study quality/risk of bias. Among 1332 records initially screened, 16 were retrieved as full-texts; of them, four papers were included in the systematic review with a total of 119,755 participants. We found that lower serum Mg concentrations were associated with a significantly higher risk of incident fractures (RR = 1.579; 95%CI: 1.216–2.051; p = 0.001; I2 = 46.9%). Our systematic review with meta-analysis suggests a strong association of serum Mg concentrations with incident fractures. Further research is needed to confirm our results among other populations and to assess whether serum Mg is potentially relevant in the prevention of fractures, which continue to increase and represent a significant health burden due to the associated disability.
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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".