THE IMPACT OF COVID-19 PANDEMIC AND LOCKDOWN ON CARDIOMETABOLIC STATUS ON PATIENTS WITHOUT EVIDENCE OF SARS COV-2 INFECTION IN THREE REGIONS OF LATIN AMERICA. A SUB-STUDY OF THE CORCOVID LATAM STUDY
Bibliographic record
Abstract
Objective: Background The purpose of the study was to evaluate regional related differences in Latin America on the impact of the pandemic in cardiometabolic patients without evidence of SARS-CoV-2 infection. Methods: A cross-sectional survey was conducted which included 38 questions about demography, clinical condition, change of health habits, medical treatments to cardio metabolic patients in outpatient follow-up. A total of 66 researchers from 13 Latin American countries enrolled patients from June 15 to July 15, 2020. The countries were divided into 3 geographic regions Region1 (NCCR) North, Central, and Caribbean Region; Region2 (AR) Andean Region; Region3 (SCR) Southern Cone Region. Isolation measures were estimated from national reports and correlated using the Spearman R coefficient. Design and method: 4216 patients were analyzed, NCCR (33.82%); AR (32.23%) SCR (33.94%). Significant differences were found between regions in the AR, SCR and NCCR, in age, unemployment, CV risk, adherence, percentage of smokers, type of diet and vaccination coverage. This analysis of habitual medication showed, that discontinued medication was greater in AR, reaching almost 30% (1 in 3 patients), to a lesser extent in NCCR and ultimately less in SCR, showing a significant difference between zones (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. Results: Significant differences were found between regions, AR is the most vulnerable region, lower income, lower educational level, higher medication discontinuation. Restrictive measures imposed by the different countries (quarantine) showed a positive correlation with the discontinuation of medication and a negative correlation with the amount of physical activity. The impact of the consequences left by this pandemic will be very profound in most Latin American countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".