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Record W2182962053 · doi:10.1093/jaoac/92.5.1541

Considerations in the Development of Biomarkers of Copper Status

2009· article· en· W2182962053 on OpenAlexafffund
Jesse Bertinato, Athina Zouzoulas

Bibliographic record

VenueJournal of AOAC International · 2009
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsHealth Canada
FundersHealth Canada
KeywordsCeruloplasminDietary Reference IntakeSuperoxide dismutaseBiomarkerMedicineCopperReference valuesReference Daily IntakePhysiologyPopulationChemistryInternal medicineEnvironmental healthBiochemistryNutrientOxidative stress

Abstract

fetched live from OpenAlex

Copper (Cu) is an essential nutrient, but a harmful metal in excess. As part of the North American Dietary Reference Intakes, the Recommended Dietary Allowance for Cu was set using a combination of biomarkers of Cu deficiency, including plasma Cu and ceruloplasmin concentrations, erythrocyte Cu/Zn superoxide dismutase activity, and platelet Cu concentration. Liver damage was the sole indicator used in setting the Tolerable Upper Intake Level. Some studies suggest that these conventional biomarkers may not be sensitive enough to detect marginal reductions or excesses of Cu that could pose a health risk. The insensitivity of conventional biomarkers casts uncertainty as to the prevalence of Cu deficiency or overload in the population and in the accuracy of current nutritional reference values for Cu. Numerous biochemical changes have been associated with alterations in Cu status, and many potential biomarkers of Cu nutriture have been proposed; yet, conventional biomarkers are still the most frequently used, underscoring the need for research efforts to substantiate the use of novel biomarkers. In this report, biomarkers of Cu status are reviewed and practical considerations in the development of novel biomarkers are discussed as diagnostic tools for assessing Cu status in humans.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.168

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.363
Teacher spread0.326 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations24
Published2009
Admission routes2
Has abstractyes

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