Post-Covid condition and clinic characteristics associated with SARS-CoV-2 infection: a 2-year follow-up to Brazilian cases
Bibliographic record
Abstract
Until January 2023, Brazil recorded 37 million COVID-19 cases despite the decrease in mortality due to mass vaccination efforts against COVID-19. The infection continues to challenge researchers and health professionals with the persistent symptoms and onset manifestations after the acute phase of the disease, namely Post-Covid Condition (PCC). Being one of the countries with the highest infection rate, Brazil must prepare for a growing number of patients with chronic health consequences of COVID-19. Longitudinal studies that follow patients over extended periods are crucial in understanding the long-term impacts of COVID-19, including potential health consequences and the effects on quality of life. We describe the clinical profile of a cohort of COVID-19 patients infected during the first year of the pandemic in Brazil and a follow-up after two years to investigate the health impacts of SARS-CoV-2 infection. The first wave of SARS-CoV-2 infection in Brazil featured extensive drug misuse, notably the ineffective COVID kit comprised of ivermectin, antimalarials and azithromycin, and elevated in-hospital mortality. In the second phase of the study, Post-Covid Condition was reported by symptomatic COVID-19 subjects across different severity levels two years after infection. Long haulers are more likely to be women, previously hospitalized, and reported a range of symptoms from muscle pain to cognitive deficit. Our longitudinal study is essential to inform public health authorities to develop strategies and policies to control the spread of the virus and mitigate its impacts on society.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".