Impacto de la pandemia COVID-19 en pacientes cardiometabólicos sin infección por SARS-CoV-2 en Latinoamérica
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: A cross-sectional survey including 38 questions about demography, clinical condition, changes in health habits, and medical treatments for cardiometabolic patients in outpatient follow-up was conducted. From June 15 to July 15, 2020, a total of 13 Latin-American countries participated in enrolling patients. METHODS: These countries were divided into 3 geographic regions: Region 1 including North, Central, and Caribbean Regions (NCCR), Region 2 including the Andean Region (AR), and Region 3 including the Southern Cone Region (SCR). 4.216 patients were analyzed, resulting in a coefficient of 33.82%, 32.23%, and 33.94% for NCCR, AR, and SCR, respectively. RESULTS: Significant differences were found between the AR, SCR, and NCCR regions. The analysis of habitual medication usage showed that discontinued use of medication was more present in AR, reaching almost 30% (p < 0.001). The main finding of this study was the negative impact that restrictive measures have on adherence to medications and physical activity: Rs = 0.84 (p = 0.0003) and Rs = 0.61 (p = 0.0032), respectively. AR was the most vulnerable region. CONCLUSION: Restrictive quarantine measures imposed by the different countries showed a positive correlation with medication discontinuation and a negative correlation with physical activity levels in patients analyzed. These findings characterize the impact of the consequences left by this pandemic. Undoubtedly, restrictive measures have been and will continue to have reverberating negative effects in most Latin-American countries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".