Minerals and Cancer: Overview of the Possible Diagnostic Value
Bibliographic record
Abstract
Cancer is the second leading cause of death worldwide and is expected to increase by one-third over the next two decades, in parallel with the growing proportion of the elderly population. Treatment and control of cancer incidence is a global issue. Since there is no clear way to prevent or cure this deadly malignancy, diagnostic, predictive, and prognostic markers for oncological diseases are of great therapeutic value. Minerals and trace elements are important micronutrients for normal physiological function of the body. They are abundant in natural food sources and are regularly included in dietary supplements whereas highly processed industrial food often contains reduced or altered amounts of them. In modern society, the daily intake, storage pools, and homeostasis of these micronutrients are dependent on certain dietary habits and can be thrown out of balance by malignancies. The current work summarizes the data on minerals and trace elements associated with abnormal accumulation or depletion states in tumor patients and discusses their value as potential tumor-associated biomarkers that could be introduced into cancer therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".