<em>GSTM1 </em>polymorphism is related to risks of nasopharyngeal cancer and laryngeal cancer: a meta-analysis
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Bibliographic record
Abstract
Background: Accumulating data have reported that GSTM1 polymorphism may be related to nasopharyngeal cancer (NPC) and laryngeal cancer (LC). This meta-analysis was performed to investigate the relationship between GSTM1 polymorphism and risks of NPC and LC. Methods: Pubmed, Embase, and China National Knowledge Infrastructure (CNKI) databases were searched for potential articles. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to evaluate the relationship of GSTM1 polymorphism with the risks of NPC and LC. I 2 >50% or P <0.05 indicates significant heterogeneity. When heterogeneity existed, the random-effects model was used to pool data, otherwise, the fixed-effects model was adopted. Publication bias was detected by Begg’s funnel plot and Egger’s regression. Quality of each study was evaluated by Newcastle-Ottawa Scale. Results: Thirty-two eligible articles were included. Pooled outcome suggested the significant relationship of GSTM1 null genotype with increased risk of LC (OR =1.28, 95% CI =1.05–1.54). Compared with hospital-based (HB) population, GSTM1 null genotype was also related to increased risk of LC (OR =1.38, 95% CI =1.06–1.80). Positive relationship of GSTM1 null genotype with enhanced risk of NPC was observed (OR =1.43, 95% CI =1.26–1.63). A similar trend was also observed in the subgroup analysis by source of control (population-based [PB]: OR =1.39, 95% CI =1.18–1.63; HB: OR =1.52, 95% CI =1.22–1.89). Conclusion: GSTM1 null genotype is related to increased risk of NPC and LC. Keywords: GSTM1 , polymorphism, NPC, LC
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it