Arsenic in Drinking Water, Transition Cell Cancer and Chronic Cystitis in Rural Bangladesh
Bibliographic record
Abstract
In earlier analyses, we demonstrated dose-response relationships between renal and lung cancer and local arsenic concentrations in wells used by Bangladeshi villagers. We used the same case-referent approach to examine the relation of arsenic to biopsy confirmed transition cell cancer (TCC) of the ureter, bladder or urethra in these villagers. As the International Agency for Research on Cancer (IARC) has conclude that arsenic in drinking water causes bladder cancer, we expected to find higher risk with increasing arsenic concentration. We used histology/cytology results from biopsies carried out at a single clinic in Dhaka, Bangladesh from January 2008 to October 2011. We classified these into four groups, TCC (n = 1466), other malignancies (n = 145), chronic cystitis (CC) (n = 844) and other benign (n = 194). Arsenic concentration was estimated from British Geological Survey reports. Odds ratios were calculated by multilevel logistic regression adjusted for confounding and allowing for geographic clustering. We found no consistent trend for TCC with increasing arsenic concentration but the likelihood of a patient with benign disease having CC was significantly increased at arsenic concentrations >100 µg/L. We conclude that the expected relationship of TCC to arsenic was masked by over-matching that resulted from the previously unreported relationship between arsenic and CC. We hypothesize that CC may be a precursor of TCC in high arsenic areas.
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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".