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 (I2 = 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 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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".