Histopathology of Cervical Cancer and Arsenic Concentration in Well Water: An Ecological Analysis
Bibliographic record
Abstract
Arsenic in drinking water is causally linked with cancer of the skin, lung, and urinary bladder, but there is very little data on a possible role for arsenic in the etiology of cervical cancer, a disease in which human papilloma virus is held to be a necessary but not sufficient cause. All histopathology results from cervical specimens from the National Institute of Cancer Research and Hospital (NICRH), Dhaka (1997-2015), and the Anowara Medical Services (2003-2015), both serving the whole of Bangladesh, were classified by cell type. Arsenic concentrations in well water in the thana of residence were estimated from systematic sampling carried out by the British Geological Survey. In a case-referent analysis arsenic estimates for cases of cervical cancer were compared with those found to have benign lesions. In this study, 3464 NICRH (CH) cervical specimens and 30,050 community medical service (CMS) specimens were available: 3329 (CH) and 899 (CMS) were recorded as malignant. Most were squamous cell carcinoma, of which 4.9% were poorly differentiated. Overall, there was no increase in cervical cancer with increasing arsenic concentration. Among those with squamous cell histology, a strong dose response was seen for poorly differentiated cancer with increasing arsenic exposure. The odds ratio increased monotonically, compared with exposure <10 μg/L, from 1.58 at 10 < 50 μg/L to 8.11 at >200 μg/L (p < 0.001). Given the high proportion of Bangladeshis using drinking water containing >50 μg/L of arsenic, the evidence that arsenic is implicated in cancer grade suggests a need for further investigation and the introduction of cervical screening 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".