Geographic Distribution of Liver and Stomach Cancers in Thailand in Relation to Estimated Dietary Intake of Nitrate, Nitrite, and Nitrosodimethylamine
Bibliographic record
Abstract
It is our working hypothesis that the high rate of the liver and gastric cancers in North and Northeast Thailand is associated with increased daily dietary intake of nitrate, nitrite, and nitrosodimethylamine (NDMA). Samples of fresh and preserved Thai foods were systematically collected and analyzed from 1988 to 1996 and from 1998 to 2005. Consumption frequencies of various food items were determined on the basis of a dietary questionnaire given to 467 adults (212 males and 255 females) from 1998 to 2005. Food consumption data for the preceding and current year were collected and intakes (day, week, and month) of nitrate, nitrite, and NDMA were calculated. The trends in liver and stomach cancer age-standardized incidence rates (ASR) in four regions of Thailand were compared with the dietary intake of nitrate, nitrite, and NDMA in those same geographic regions. Mean daily intakes of nitrate of 155.7 mg/kg, of nitrite of 7.1 mg/kg, and of NDMA of 1.08 microg/kg per day were found. Significant differences in dietary nitrate, nitrite, and NDMA intakes were seen between various Thai regions (P < 0.0001), and these corresponded to the variations in liver and stomach cancer ASR values between the regions. Dietary factors are likely to play key roles in different stages of liver and stomach carcinogenesis in Thailand.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".