[Cardiovascular disease in times of COVID-19].
Bibliographic record
Abstract
There are increasing reports of a drastic drop in consultations and cardiovascular procedures (including urgencies and emergencies) in regions affected by the COVID-19 pandemic, with a consequent marked increase in total mortality that is not fully explained by COVID-19. Cardiovascular disease leads the ranking in deaths in adults in Argentina with 280 deaths per day, and in recent decades we have reduced its mortality by 20-30% through various evidence-based interventions. Herein we conducted predictive analyses to understand what could be the consequences of a worse implementation of those interventions. We estimate that less control of cardiovascular risk factors from April to October 2020 could cause up to 10 500 new preventable cases of cardiovascular disease. In terms of myocardial infarction, a drop from 40% to 60% of the reperfusion treatment could increase mortality by 3% to 5%. A marginal 10% to 15% increase in relative risk of cardiovascular death would be equivalent to an excess of 6000 to 9000 preventable deaths. In conclusion, given the high prevalence and fatality of cardiovascular disease, even a small negative impact on the efficacy of its care will translate into large numbers of people affected in Argentina. It is necessary to inform the authorities and educate the public so cardiovascular diseases and their risk factors remain a health priority, as long as resources exist and minimizing the risk of contagion and spread of the virus.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".