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Record W2080266388 · doi:10.14740/cr315w

The Relationship Between Blood Monocyte Count and Coronary Artery Ectasia

2014· article· en· W2080266388 on OpenAlexvenueno aff
Mehmet Demir, Canan Demir, Serdar Keçeoğlu

Bibliographic record

VenueCardiology Research · 2014
Typearticle
Languageen
FieldMedicine
TopicKawasaki Disease and Coronary Complications
Canadian institutionsnot available
Fundersnot available
KeywordsCoronary artery ectasiaMedicineMonocyteMean platelet volumeInternal medicineCardiologyCoronary artery diseasePathophysiologyEctasiaPlateletGastroenterologyAbsolute neutrophil countLymphocyteMyocardial infarctionCoronary angiography

Abstract

fetched live from OpenAlex

BACKGROUND: The pathophysiology of coronary artery ectasia (CAE) has not been clearly identified, although multiple abnormalities including arteritis, endothelial dysfunction, and atherothrombosis have been reported. It is known that monocytes play an important role in inflammation, atherosclerosis and cardiovascular disease. We aimed to compare the numbers of monocyte counts of the CAE patients versus controls. METHOD: This study included 84 CAE patients (40 male, mean age 55.4 ± 9.7 years) and 30 controls (10 male, mean age 57.86 ± 11.6 years). Concurrent routine biochemical tests and neutrophil, lymphocyte, monocyte count and mean platelet volume (MPV) on whole blood count were performed for these participants. These parameters were compared between groups. RESULTS: Baseline characteristics of the study groups were comparable. CAE patients had a higher MPV value and monocyte count than controls (8.8 ± 0.2 vs. 6.2 ± 1.6 fL and 732 ± 88 vs. 321 ± 75 cell/μL; both P < 0.001, respectively). CONCLUSION: As a result, our study revealed a relationship between monocyte count and MPV in patients with CAE.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.145
GPT teacher head0.396
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2014
Admission routes1
Has abstractyes

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