Impact of the Pandemic on NonInfected Cardiometabolic Patients: A Survey in Countries of Latin America—Rationale and Design of the CorCOVID LATAM Study
Bibliographic record
Abstract
BACKGROUND: The first case of coronavirus 2019 (COVID-19) in Latin America was detected on February 26th, 2020, in Brazil. Later, in June, the World Health Organization announced that the focus of the outbreak had shifted to Latin America, where countries already had poor control of indicators of noncommunicable diseases (NCDs). Concerns about coronavirus infection led to a reduced number of visits and hospitalizations in patients with NCDs, such as cardiovascular disease, diabetes, and cancer. There is a need to determine the impact of the COVID-19 pandemic on patients who have cardiometabolic diseases but do not have clinical evidence of COVID-19 infection. METHODS: The CorCOVID LATAM is a cross-sectional survey of ambulatory cardiometabolic patients with no history or evidence of COVID-19 infection. The study will be conducted by the Interamerican Society of Cardiology. An online survey composed of 38 questions using Google Forms will be distributed to patients of 13 Latin American Spanish-speaking countries from June 15th to July 15th, 2020. Data will be analyzed by country and regions. Seven clusters of questions will be analyzed: demographics, socioeconomic and educational level, cardiometabolic profile, lifestyle and habits, body-weight perception, medical follow-up and treatments, and psychological symptoms. RESULTS: Final results will be available upon completion of the study. CONCLUSIONS: The present study will provide answers regarding the impact of the COVID-19 pandemic on noninfected cardiometabolic patients. Data on this topic are scarce, as it is an unprecedented threat, without short-term solutions.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".