The Levels of Zinc and Molybdenum in Hair and Food Grain in Areas of High and Low Incidence of Esophageal Cancer: A Comparative Study
Bibliographic record
Abstract
The outcome of different studies on the role of Zn & Mo in esophageal cancer (EC) is conflicting. Here, the levels of those elements in hair as well as food grain of two different ethnic populations across two continents have been studied to explore their role in EC. Two different ethnic populations are taken from (i) Eastern Cape, South Africa (RSA), an area of very high incidence of EC and (ii) West Bengal, India, an area of low incidence of that disease. Each ethnic population is divided into two groups: case and control (n=30 for all groups). Hair samples from all groups and food grain from RSA and India are analyzed for Zn & Mo content. This study shows a strong correlation between reduced levels of those elements in hair and the development of EC in RSA (both Zn &Mo: p < 0.0001), though it is only suggestive in Indian context (both Zn & Mo p ? 0.05). Interestingly, control group of RSA shows significantly reduced level of those elements in hair even with respect to Indian case group (Zn: p<0.001 & Mo: p< 0.00001). Food grain from RSA has significantly reduced level of those elements with respect to India (both Zn & Mo: p <0.0001). This deficiency of Zn & Mo in food grains can be correlated to the deficiency of those elements in hair of RSA population. The deficiency of Zn & Mo can be correlated to the development of EC.
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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".