Особенности популяции, диагностика, вторичная профилактика и антиангинальная терапия у пациентов с диагнозом стабильная стенокардия
Bibliographic record
Abstract
Aim. To assess compliance with international clinical guidelines on coronary heart disease (CHD) secondary prevention, as well as on stable angina pectoris diagnostics and treatment, in Russian clinical practice settings. Material and methods . Among 5000 randomly selected out-patients visiting Moscow City Cardiology Dispanser (MCCD) in 2001, 1840 patients with stable angina diagnosis were identified. Out-patient medical documents were studied, and information on CHD risk factor (RF) registration, clinical diagnosis, clinical and laboratory tests, therapy (agents and their doses), associated diseases and their treatment, was analyzed. Results. Moscow doctors, in spite of existing international and national clinical guidelines, do not use objective diagnostic tests and rely on subjective complaints, while assessing disease severity and determining management tactics, in every second patient (49.2%). The doctors inadequately address RF in stable angina patients, fail to modify RF effectively, and do not attain RF target levels. The principal problems of pharmacotherapy are inadequately low use of statins in CHD secondary prevention (1.8% at out-patient clinics; 6.4% Ц at MCCD), as well as use of minimal effective doses for most antianginal agents. Coronarography, coronary angioplasty, and bypass surgery rates are substantially lower than those in Eastern European countries. Conclusion. The recommendations of national and international guidelines on CHD secondary prevention, stable angina diagnostics and treatment, are still fulfilled inadequately.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".