Improving the Quality of Life of Patients with Arterial Hypertension in Diabetes Mellitus
Bibliographic record
Abstract
Currently, there are no recommendations on the features of detecting early signs of CHF in patients with diabetes mellitus. And they are necessary, taking into account that in case of diabetes mellitus type 2 (more than 95% of all patients with diabetes mellitus), the overwhelming number of patients are overweight and obesity, which can affect the validity of the 6-minute walk test and scale clinical state assessment (SHOX) in the modification.In the treatment of patients with CHF, the “gold” standard is the use of angiotensin-converting enzyme inhibitors (ACE inhibitors) according to the third edition of the recommendations on the diagnosis and treatment of CHF. Antagonists of receptors for angiotensin II type 1 (APA II) remain at the same time reserve drugs. Adherence to these standards of patients in different regions of the Russian Federation varies over a rather wide range, and there is almost no information about the compliance of patients with diabetes mellitus with prescribed drug therapy in the presence of CHF.Thus, the need to identify the frequency of CHF in patients with type 2 diabetes, clarifying the features of this diagnosis and optimizing the treatment of such patients is beyond doubt.Objective: to clarify the frequency of chronic heart failure in patients with type 2 diabetes in the specialized | department of the hospital and unorganized urban population, to assess the adequacy of its diagnosis and treatment.Research Objectives: Identify the prevalence of chronic heart failure in patients with type 2 diabetes.To assess the level of diagnosis of chronic heart failure in patients with type 2 diabetes.To analyze the state of drug therapy for patients with type 2 diabetes in the presence of chronic serum
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".