Association between Praziquantel and Cholangiocarcinoma in Patients Infected with Opisthorchis viverrini: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The liver fluke, Opisthorchis viverrini, and the associated incidence of subsequent cholangiocarcinoma (CCA) are still a public health problem in Thailand, and praziquantel (PZQ) remains the antihelminthic drug of choice for treatment. Evidence in hamsters shows that repeated infection and PZQ treatments could increase the risk of CCA. However, the existing evidence in humans is inconclusive regarding increased risk of CCA with frequency of PZQ intake. OBJECTIVES: To investigate the relationship between number of repeated PZQ treatments and CCA in patients with O viverrini infection. MATERIALS AND METHODS: The reviewed studies were searched in EMBASE, MEDLINE, ProQuest, PubMed and SCOPUS from inception to October, 2012 using prespecified keywords. The risk of bias (ROB) of included studies was independently assessed by two reviewers using a quality scale from the Newcastle-Ottawa Scale (NOS). Risk effect of PZQ was estimated as a pooled odds ratio (OR) with its 95% confidence interval (95%CI) in the random-effects model using DerSimonian and Laird's estimator. RESULTS: Three studies involving 637 patients were included. Based on the random effects model performed in two included studies of 237 patients, the association between PZQ treatments and CCA was not statistical significant with a pooled OR of 1.8 (95%CI; 0.81 to 4.16). CONCLUSIONS: The present systematic review and meta-analysis provides inconclusive evidence of risk effect of PZQ on increasing the risk of CCA and significant methodological limitations. Further research is urgently needed to address the shortcomings found in this review, especially the requirement for histological confirmation.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".