Arsenic Induced Gallbladder Cancer Risk in Eastern Part of Indo-Gangetic Plains
Bibliographic record
Abstract
Abstract BackgroundIn recent times Gallbladder cancer (GBC) incidences increased many folds in India. Majority of GBC cases are being reported from arsenic hotspots identified in Bihar. MethodsIn this prospective study volunteers were selected who underwent surgery in our cancer institute. There were 11 control benign gallbladder cases and 28 confirmed gallbladder cancer cases. Their biological samples such as blood, gallbladder tissue, gallbladder stone, bile and hair samples were collected for arsenic estimation. Moreover, n=512 gallbladder cancer patients blood samples were evaluated for the presence of arsenic to understand exposure level in the population. ResultsA significantly high arsenic concentration (p<0.05) was detected in the blood samples, gallbladder tissue, gallstones, bile and hair samples in comparison to the control group. Moreover, n=512 blood samples of GBC patients had significantly very high arsenic concentration. ConclusionsThe study shows very high arsenic concentration observed in the blood, gallbladder tissue, gallbladder stone, bile and hair samples in GBC cases in comparison to the benign control cases indicates the correlation between chronic arsenic exposure and gallbladder cancer incidences in eastern Indo-Gangetic plains region. The study also makes an attempt to establish the likely correlation/association between arsenic exposure and gallbladder cancer disease.
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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 | 0.000 |
| 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) | 0.003 | 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".