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Record W4297964727 · doi:10.1093/jalm/jfac079

Relationship between Plasma Zinc and Red Blood Cell Zinc Levels in Hospitalized Patients

2022· article· en· W4297964727 on OpenAlexaff
Stefan Rodic, Christopher R. McCudden, Carl van Walraven

Bibliographic record

VenueThe Journal of Applied Laboratory Medicine · 2022
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsInstitute for Clinical Evaluative SciencesCanadian Electricity AssociationOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsZincMedicineInternal medicineGastroenterologyNeutrophil to lymphocyte ratioInflammationLymphocyteChemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Patient zinc stores are quantified with plasma or red blood cell (RBC) measures. The relationship between these 2 measures of zinc status has not been determined in a broad population of hospitalized patients. METHODS: Both plasma zinc and RBC zinc were prospectively collected and measured in 252 consenting patients admitted urgently to hospital. Plasma and RBC zinc levels were measured within 48 h of admission. We collected demographic, vitals, and laboratory data for use in multivariate regression models that included markers of acute disease severity and systemic inflammation. RESULTS: Plasma zinc and RBC zinc levels were low in 63% and 10% of hospitalized patients, respectively. Categorized zinc levels based on normal intervals for plasma and RBC zinc values were not related (χ2 0.47 [2 df] P = 0.79). The Pearson correlation coefficient between plasma zinc and RBC zinc was -0.09 (P = 0.15). After adjustments for multiple clinical covariates, the correlation coefficient remained insignificant (r = -0.11, P = 0.08). Plasma zinc was inversely associated with markers of inflammation including the neutrophil-to-lymphocyte ratio and temperature. CONCLUSIONS: Patient-specific plasma and RBC zinc are unrelated in hospitalized patients, possibly due to decreased values with acute illness seen in the former but not the latter. Future studies are required to determine which of these measures best predicts outcomes in hospitalized patients.

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)0.003
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 score0.010
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.285
Teacher spread0.258 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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