Medicinal Inorganic Chemistry: Correcting Essential Metal‐Ion Deficiencies
Bibliographic record
Abstract
Abstract Essential metal ions are those for which there is a known requirement for good health in the body. When dietary intake does not, or cannot, maintain adequate tissue stores of an essential trace or ultratrace metal ion, metal‐ion supplementation may be necessary. Simple salts of metal ions are often poorly tolerated, not well absorbed, or too rapidly excreted (or all three of these); therefore, appropriate ligand binding of the particular metal ion can significantly improve the success of supplementation efforts. The most common deficiency disorder, iron‐deficiency anemia, is now treated with a number of iron complexes, e.g., Ferrochel™ and ferrous gluconate, that are superior to simple iron salts such as ferrous sulfate; others, such as ferric maltol, are being proposed as useful substitutes. Marginal deficiencies of trace and ultratrace elements, such as zinc, copper, manganese, and chromium, are purported to have long‐term negative health effects, especially in aging or ill populations. Defining marginal deficiency is difficult, owing to lack of reliable and accurate biomarkers for the determination of trace element status.
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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) | 0.013 | 0.004 |
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".