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Record W4298758626

Co-morbidity and cardio-metabolic risk factors in patients with osteoarthrosis

2009· article· en· W4298758626 on OpenAlexaboutno aff
N. A. Artemenko

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2009
Typearticle
Languageen
FieldMedicine
TopicHealthcare Systems and Public Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicineCardiology
DOInot available

Abstract

fetched live from OpenAlex

Aim. To study co-morbidity prevalence, as well as metabolic effects on atherosclerosis progression and cardiovascular risk, in patients with osteoarthrosis (OA).Material and methods. In total, 83 patients with confirmed OA diagnosis (American College of Rheumatology criteria). The age of participants was 45-70 years (mean age 58,35+12,5 years), and OA duration was 1-24 years (mean duration 9,8±4,7 years). The examination included pain syndrome assessment with visual analog scale (VAS), calculation of WOMAC (Western Ontario and McMaster Universities) index and body mass index (BMI), measurement of waist and hip circumference (WC, HC) and WC/HC ratio. Joint ultrasound examination (Phillips HD-11) was used to assess periarticular tissue status and visualise articular cartilage and bone surfaces. Serum levels of C-reactive protein (CRP) and interleukin-6 (IL-6) were measured by immune-enzyme method.Results. Obesity was diagnosed in 51 OA patients (67,64%). These participants demonstrated higher synovitis prevalence, higher values of algo-functional indices, and elevated IL-6 and CRP levels, as well as higher frequency of cardiovascular disease and cardiovascular events in anamnesis.Conclusion. In OA patients, obesity was more prevalent. Compared to those with BMI<30 kg/m2, obese individuals with OA showed higher levels of triglycerides and CRP, lower concentration of high-density lipoproteins, and higher prevalence of Caro index <0,33 (insulin resistance marker). Therefore, the combination of OA and obesity was characterised by clustering of cardiovascular risk factors.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.170
GPT teacher head0.532
Teacher spread0.361 · 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 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

Citations0
Published2009
Admission routes1
Has abstractyes

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