Some aspects of comorbidity in hospitalized patients of a therapeutic hospital
Bibliographic record
Abstract
Objective - to analyze the risk factors (RF) of noncommunicable diseases depending on the comorbidity index (CI) in hospitalized patients. Materials and methods. A cross-sectional study was performed at the Altai Regional Hospital for War Veterans. 128 people were invited to take part in the study during the month in the therapeutic department, 100 people agreed (78.1% response). The average age is 77.9±8.3 years, 48% of women, 52% of men. A general clinical examination, RF analysis of noncommunicable diseases, psychosocial factors, Montreal Cognitive Function Scale (MoCA test), and an examination by a neurologist to detect encephalopathy were performed. Based on the Charlson CI data, patients were divided into 3 groups: group 1 - CI 1-2 points - 46%, group 2 - CI 3-4 points - 38%, group 3 - CI 5 and more points - 16%. Results. Regardless of gender, CI 5 or more was more common than CI 1-2 by 16.8%; among men, CI 5 and more occurred more often than CI 3-4 by 17.8%; middle-aged persons were only in the group with CI 1-2. In patients with CI 5 or more, compared with patients with CI 1-2, there was a higher frequency of such RFs as obesity (by 29.9%, all persons with CI 5 or more had abdominal obesity), social isolation (by 29.7%), type D personality (by 36.5%), as well as cognitive impairment (by 28.5%) and encephalopathy (by 32.9%). Depression was found 26.1% more often in patients with CI 3-4 than in patients with CI 1-2. Conclusion. Comorbidity is not a mandatory condition characteristic of an aging population, it is primarily the result of individual behavior. Therefore, the identification and correction of RF of noncommunicable diseases, especially in conditions of comorbidity, seems to be an urgent task, which determines the success of treatment in this category of patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".