Thinking beyond Opisthorchis viverrini for risk of cholangiocarcinoma in the lower Mekong region: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Cholangiocarcinoma (CCA) is a fatal bile duct cancer associated with infection by the liver fluke, Opisthorchis viverrini, in the lower Mekong region. Numerous public health interventions have focused on reducing exposure to O. viverrini, but incidence of CCA in the region remains high. While this may indicate the inefficacy of public health interventions due to complex social and cultural factors, it may further indicate other risk factors or interactions with the parasite are important in pathogenesis of CCA. This systematic review aims to provide a comprehensive analysis of described risk factors for CCA in addition to O. viverrini to guide future integrative interventions. MAIN BODY: We searched five international and seven Thai research databases to identify studies relevant to risk factors for CCA in the lower Mekong region. Selected studies were assessed for risk of bias and quality in terms of study design, population, CCA diagnostic methods, and statistical methods. The final 18 included studies reported numerous risk factors which were grouped into behaviors, socioeconomics, diet, genetics, gender, immune response, other infections, and treatment for O. viverrini. Seventeen risk factors were reported by two or more studies and were assessed with random effects models during meta-analysis. This meta-analysis indicates that the combination of alcohol and smoking (OR = 11.1, 95% CI: 5.63-21.92, P < 0.0001) is most significantly associated with increased risk for CCA and is an even greater risk factor than O. viverrini exposure. This analysis also suggests that family history of cancer, consumption of raw cyprinoid fish, consumption of high nitrate foods, and praziquantel treatment are associated with significantly increased risk. These risk factors may have complex relationships with the host, parasite, or pathogenesis of CCA, and many of these risk factors were found to interact with each other in one or more studies. CONCLUSIONS: Our findings suggest that a complex variety of risk factors in addition to O. viverrini infection should be addressed in future public health interventions to reduce CCA in affected regions. In particular, smoking and alcohol use, dietary patterns, and socioeconomic factors should be considered when developing intervention programs to reduce CCA.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".