Magnesium intake and lung cancer risk: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract. Magnesium may reduce the risk of lung cancer by affecting cell proliferation, inflammation and by preserving lung function; however, the results of epidemiological studies on the potential benefits of magnesium in lung pathology are inconclusive. We conducted this meta-analysis to investigate the association between magnesium intake and the risk of lung cancer. A total of 5 studies were extracted from PubMed, SCOPUS, and the Cochrane Review (to May 2018). These studies involved 58,5821 participants with 8,977 lung cancer cases. The pooled relative risk (RR) indicated a significant association between lung cancer incidence and magnesium intake (RR = 0.88, 95% CI = 0.79 to 0.98; p = 0.018). To investigate the cause of heterogeneity of these studies (I 2 = 75.8%, p < 0.001), we performed a subgroup analysis which was affected by the mean dose of magnesium intake, where doses of magnesium intake lower than 300 mg/d significantly decreased lung cancer risk (RR = 0.83, 95% CI = 0.70 to 0.99; p = 0.034). Increasing magnesium intake doses to over 300 mg/d did not reduce the incidence of lung cancer (RR = 0.89, 95% CI = 0.78 to 1.01; p = 0.076). Our meta-analysis suggests that magnesium intake of less than 300 mg/d may have protective effects in lung cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".