Association between ranitidine use with potential NDMA impurities and risk of cancer in Korea
Bibliographic record
Abstract
N-Nitrosodimethylamine (NDMA) detected above the acceptable level in ranitidine products has been a great global concern. To examine the risk of cancer among people treated with ranitidine, we conducted a cohort study using the National Health Insurance Service-National Sample Cohort data (2002-2015) of South Korea. Patients were aged 40 or above as of January 2004 and began receiving ranitidine or other histamine-2 receptor antagonist (H2RA), active comparator, without a history of H2RAs prescription during the prior 2-years. The lag time was designated up to 6 years. The outcomes were an overall incident cancer risk and the risk of major single cancers during the follow-up. The association between ranitidine use and cancer risk was examined by Cox regression model. After exclusion and propensity score matching, 25,360 patients were available for analysis. The use of ranitidine was not associated with the overall cancer risk and major individual cancers [overall cancer: incidence rate per 1000 person-years, 2.9 vs 3.0 among the ranitidine users and other H2RAs users, respectively; adjusted hazard ratio (HR) and 95% confidence interval (95% CI) for all cancers, 0.98 (0.81-1.20)]. The higher cumulative exposure to ranitidine did not increase the cancer risk. Given the insufficient follow-up period, these findings should be interpreted carefully.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".