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Record W1519189337 · doi:10.1002/0470862106.ia475

Medicinal Inorganic Chemistry: Metallotherapeutics for Chronic Diseases

2005· other· en· W1519189337 on OpenAlexaff
Katherine H. Thompson

Bibliographic record

VenueEncyclopedia of Inorganic Chemistry · 2005
Typeother
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineOsteoporosisRheumatoid arthritisDiabetes mellitusType 2 Diabetes MellitusPharmacologyIntensive care medicineInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Abstract Metal‐containing therapeutic agents designed for use in chronic disorders, such as gastrointestinal dyspepsia, bipolar disease, diabetes mellitus, osteoporosis, and arthritis, present additional challenges to the medicinal chemist. Beyond the immediate toxicity issues seen with acute disease treatments, chronic use of metallotherapeutic agents may result in metal ion accumulation or in gradually developing intolerance that can require discontinuation of the drug. Metallotherapeutic agents that have passed the rigorous testing required for chronic use include bismuth subsalicylates, for a variety of gastrointestinal disorders; lithium carbonate, for bipolar disease; lanthanum carbonate, for end‐stage renal disease; strontium ranelate, for osteoporosis; and the gold‐based antiarthritic, Auranofin™, although the last has fallen out of favor. Chromium picolinate is an approved nutritional supplement, a designation that allows less stringent testing than for new chronic‐use drugs. Among those compounds not currently approved for clinical use, but showing promise, the copper – indomethacin and zinc – indomethacin complexes, for inflammation resulting from rheumatoid arthritis; vanadium compounds, for glucose‐ and lipid‐lowering in type 2 diabetes mellitus; and selenium compounds, as possible cancer preventatives, are worthy of consideration.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.110
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0340.000

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.

Opus teacher head0.008
GPT teacher head0.274
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2005
Admission routes1
Has abstractyes

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