Risk Factors Associated with Nasopharyngeal Cancer Incidences in Indonesia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: To determine the risk factors associated the incidence of NPC, particularly in Indonesia. METHODS: This systematic review and meta-analysis was conducted according to PRISMA statement. Database including PubMed, Scopus, Science Direct, Web of Science, and GARUDA were retrieved. Newcastle-Ottawa scale was used to assess the quality of published study and analyse the risk of bias of included study. Random-effect model and reported pooled Odds Ratio (OR) with 95%CI was carried out in our meta-analysis. RESULTS: A pooled of 7 studies were included in our study which included 764 participants. We found that female gender was not associated with the incidences of NPC (OR 1.45, 95% CI: 0.61-3.45, p=0.40), and smoking was highly increased the incidence of NPC (OR 4.39 95% CI (0.79-24.40), but not statistically significant (p=0.09). Furthermore, salted fish consumption and some HLA alleles were associated with increased risk. CONCLUSION: The incidence of NPC is not associated with female gender nor smoking habits. However, the risk of NPC is higher for those who consume salted fish and have some susceptible HLA alleles. Further investigations in larger studies are needed to confirm these findings.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".