Risk Factors for Cholangiocarcinoma in Thailand: A SystematicReview and Meta-Analysis
Bibliographic record
Abstract
Background and objective: Cholangiocarcinoma remains a serious public health concern in Thailand. While many of the risk factors for cholangiocarcinoma in western countries are well-recognized, it remains unclear whether they are the same in Thailand. We set out to investigate the risk factors for cholangiocarcinoma in Thailand. Methods: Starting March 4, 2016, we reviewed studies found using pre-specified keywords on SCOPUS, Pro Quest Science Direct, PubMed, and online public access catalog of Khon Kaen University. Two review authors independently screened studies for inclusion criteria, extracted data, and assessed the studied Risk of Bias. The Newcastle-Ottawa Scale and the Joanna Briggs Institute Critical Appraisal Tools were used to assess the quality of included studies. The risk effects of factors were estimated as a pooled adjusted odds ratio with a 95% confidence interval. The heterogeneity of results was considered using the I-square, Tau-square and Chi-square statistics. Results: A strong association was found between cholangiocarcinoma and age, Opisthorchis viverrini infection, eating raw cyprinoid fish, family history of cancer, liquor consumption, and taking praziquantel. There was only a mild association found between eating nitrite-containing foods, fresh vegetables, education, smoking behavior, and sex. No association was found between cholangiocarcinoma and eating fermented fish (Pla-ra), northeastern Thai or Chinese sausage, sticky rice, meat, chewing betel nut, or eating fruit. There were two protective factors including fresh vegetables consumption and education attainment. Conclusion: There are unique risk factors of cholangiocarcinoma in Thailand, including age, Opisthorchis viverrini infection, eating raw cyprinoid fish, family history of cancer, liquor consumption, and taking praziquantel.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".