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Mental disorders in patients with osteoarthritis combined with diabetes mellitus in the practice of a family doctor

2025· article· en· W7083709817 on OpenAlexaboutno aff

Bibliographic record

VenueINTERNATIONAL JOURNAL OF ENDOCRINOLOGY (Ukraine) · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBacterial Infections and Vaccines
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyOsteoarthritisDiabetes mellitusPsychosocialDepression (economics)ComorbidityMental health

Abstract

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Background. Osteoarthritis is a chronic degenerative joint disease, often accompanied by pain, limited mobility and reduced quality of life. Diabetes mellitus, in turn, is a metabolic disease with systemic complications. The coexistence of these two pathologies is a frequent clinical phenomenon that aggravates the course of each disease. The purpose of the study was to assess the level of mental health in patients with osteoarthritis who have concomitant diabetes mellitus, to identify the main psycho-emotional disorders and develop recommendations for the diagnosis, treatment, prevention and improvement of psychosocial support for such people. Materials and methods. The study included 120 patients who were divided into three groups: group I — osteoarthritis without concomitant type 2 diabetes mellitus (T2DM); group II — T2DM without osteoarthritis; group III — comorbidity of osteoarthritis and T2DM. The following research methods were used: the Hospital Anxiety and Depression Scale (HADS) — to identify anxiety and depressive symptoms; Beck Depression Inventory-II — to quantify the level of depression; SF-36 Health Survey — to analyze quality of life, in particular mental and physical components; Mini-Mental State Examination (MMSE) — to screen for cognitive disorders; Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) — to assess the seve­rity of pain syndrome, stiffness and functional limitation. Results. Among patients with osteoarthritis (group I), clinically significant manifestations of depression (> 11 points on the HADS) were detected in 32.5 % of cases, anxiety — in 40 %. Among patients with T2DM (group II), depression — in 27.5 %, anxiety — in 35 %. The highest level of depressive and anxiety manifestations was observed in patients with comorbidity of osteoarthritis and T2DM (group III): depression — 55 %, anxiety — 60 %. According to the MMSE, mild cognitive impairment was detected in 15 % of patients from group I, 22.5 % from group II and 37.5 % from group III. Patients with comorbidity of osteoarthritis and T2DM demonstrated the worst indicators of physical functioning and mental health according to the SF-36 scale. Decreased quality of life was mainly associated with pain syndrome, mobility limitation and emotional exhaustion. A positive correlation was established between the level of glycated hemoglobin and the severity of depression (r = 0.42; p < 0.01). A relationship was also found between the severity of pain syndrome (WOMAC) and the level of anxiety (r = 0.51; p < 0.01). The statistical power of the analysis of variance with a sample of 120 patients (3 groups of 40 patients) and a moderately high effect (f = 0.35) is 93.4 %. This indicates a high probability of detecting real intergroup differences, i.e. the sample size is statistically justified for the stated study design. Conclusions. Comorbid course of osteoarthritis and type 2 diabetes is associated with a high risk of developing depressive and anxiety disorders, which significantly reduces the quality of life of patients and requires not only medical, but also psychological support. Depressive symptoms correlate with the intensity of pain and impaired metabolic control, which complicates the overall clinical course of diseases. Socio-psychological factors (stress, loneliness) play an important role in the formation of psycho-emotional disorders in patients with comorbidity.

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 score0.058
Threshold uncertainty score0.302

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)0.0000.000

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 head0.005
GPT teacher head0.246
Teacher spread0.241 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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