Selenium and Lung Cancer: A Systematic Review and Meta Analysis
Bibliographic record
Abstract
BACKGROUND: Selenium is a natural health product widely used in the treatment and prevention of lung cancers, but large chemoprevention trials have yielded conflicting results. We conducted a systematic review of selenium for lung cancers, and assessed potential interactions with conventional therapies. METHODS AND FINDINGS: Two independent reviewers searched six databases from inception to March 2009 for evidence pertaining to the safety and efficacy of selenium for lung cancers. Pubmed and EMBASE were searched to October 2009 for evidence on interactions with chemo- or radiation-therapy. In the efficacy analysis there were nine reports of five RCTs and two biomarker-based studies, 29 reports of 26 observational studies, and 41 preclinical studies. Fifteen human studies, one case report, and 36 preclinical studies were included in the interactions analysis. Based on available evidence, there appears to be a different chemopreventive effect dependent on baseline selenium status, such that selenium supplementation may reduce risk of lung cancers in populations with lower baseline selenium status (serum<106 ng/mL), but increase risk of lung cancers in those with higher selenium (≥ 121.6 ng/mL). Pooling data from two trials yielded no impact to odds of lung cancer, OR 0.93 (95% confidence interval 0.61-1.43); other cancers that were the primary endpoints of these trials, OR 1.51 (95%CI 0.70-3.24); and all-cause-death, OR 0.93 (95%CI 0.79-1.10). In the treatment of lung cancers, selenium may reduce cisplatin-induced nephrotoxicity and side effects associated with radiation therapy. CONCLUSIONS: Selenium may be effective for lung cancer prevention among individuals with lower selenium status, but at present should not be used as a general strategy for lung cancer prevention. Although promising, more evidence on the ability of selenium to reduce cisplatin and radiation therapy toxicity is required to ensure that therapeutic efficacy is maintained before any broad clinical recommendations can be made in this context.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".