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Record W3011133909 · doi:10.26355/eurrev_201903_17259

Hyperglycemia effect on red blood cells indices.

2019· article· en· W3011133909 on OpenAlexaff
Bader N. Alamri, Aban Bahabri, A A Aldereihim, M Alabduljabbar, M M Alsubaie, Dhekra Alnaqeb, Ebtehal Almogbel, N.S. Metias, Omar Alotaibi, Khalid Al‐Rubeaan

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMean corpuscular volumeGlycemicMean corpuscular hemoglobin concentrationMedicineMean corpuscular hemoglobinRed blood cell distribution widthHemoglobinDiabetes mellitusRed blood cellInternal medicineBlood cellType 2 diabetesEndocrinologyPhysiologyGastroenterology

Abstract

fetched live from OpenAlex

OBJECTIVE: Hyperglycemia has an effect on all body tissues; one of them is the bone marrow. This effect is related to protein glycation and other chemical and physiological changes of red blood cells (RBCs). The aim of this study was to assess the effect of hyperglycemia on different RBCs indices along with evaluating these changes in the normal physiology and chronic diabetes complication pathology. PATIENTS AND METHODS: This is a cross-sectional hospital-based study of 1000 type 2 Saudi diabetic patients without any hematological diseases. Patients were fully evaluated clinically and biochemically with full blood hematological parameters assessment. The studied cohort matched the general characteristics of Saudi type 2 diabetic patients. RESULTS: This study shows that hyperglycemia increases the red blood cells count, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC). Red blood cell distribution width (RDW) was negatively correlated with poor glycemic control. Concurrently, the presence of micro and macroangiopathies with hyperglycemia shortens the lifespan of RBCs. CONCLUSIONS: We conclude that hyperglycemia has an imposing effect on RBCs count and its physiological function, which can be normalized effectively with good glycemic control.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.001

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.

Opus teacher head0.007
GPT teacher head0.201
Teacher spread0.194 · 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

Citations66
Published2019
Admission routes1
Has abstractyes

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