Impact of the first wave of coronavirus disease 2019 (COVID-19) pandemic on the diagnosis of heart disease in the Russian Federation: results from the IAEA Nuclear Cardiology Protocols Study (INCAPS)
Bibliographic record
Abstract
Aim . To assess the impact of the first wave of coronavirus disease 2019 (COVID-19) pandemic on the diagnosis of heart disease in the Russian Federation. Material and methods . Fifteen Russian medical centers from 5 cities took part in an online survey organized by the Division of Human Health of the International Atomic Energy Agency (IAEA), containing questions regarding alterations in cardiovascular procedure volumes resulting from COVID-19 in March-April 2020. Results. A number of outpatients undergoing cardiac diagnostic procedures was noted in 80% of clinics. Cardiovascular procedure volumes in the period from March 2019 to March 2020 in general decreased by 9,5%, and from March 2019 to April 2020, by 56,5%. Stress electrocardiography decreased by 38,4%, stress echocardiography by 72,5%, stress single-photon emission computed tomography by 66,9%, computed tomography angiography by 49,7%, magnetic resonance imaging by 42,7%, invasive coronary angiography by 40,7%. The decrease in diagnostic procedure volumes in selected regions (Tomsk Oblast, Kemerovo Oblast, Tatarstan) was not so pronounced compared to Moscow and St. Petersburg (-20,7%, -75,2%, -93,8% in April 2020, respectively, p<0,001). Conclusion . The first wave of the COVID-19 pandemic caused a sharp decrease in the number of diagnostic cardiac procedures in Russia. This has potential longterm implications for patients with cardiovascular disease. Understanding these implications can help guide diagnostic strategies during the ongoing COVID-19 pandemic and minimize the future losses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".