Clinical and serological profile of asymptomatic and non-severe symptomatic COVID-19 cases: Lessons from a longitudinal study in primary care in Latin America
Bibliographic record
Abstract
BACKGROUND: Chile has one of the highest incidences of COVID-19 infection in the world. Primary care can play a key role in early detection and containment of the disease. There is a lack of information on the clinical profile of patients with suspected COVID-19 in primary care, and controversy on the effectiveness of rapid serologic tests in the diagnosis and surveillance of the disease. AIM: To assess the effectiveness of rapid serologic testing in detection and surveillance of COVID-19 cases in primary care. DESIGN & SETTING: A longitudinal study was undertaken, which was based on a non-random sample of 522 participants, including 304 symptomatic patients and 218 high-risk asymptomatic individuals. They were receiving care at four primary health clinics in an underserved area in Santiago, Chile. METHOD: The participants were systematically assessed and tested for COVID-19 with reverse transcriptase-polymerase chain reaction (RT-PCR) and serology at baseline, and were followed clinically and serologically for 3 weeks. RESULTS: The prevalence rate of RT-PCR confirmed COVID-19 cases were 3.5 times higher in symptomatic patients (27.5%; 95% confidence interval [CI] = 22.1 to 32.8) compared with asymptomatic participants (7.9%; 95% CI = 4.3 to 11.6). Similarly, the immune response was significantly different between both groups. Sensitivity of serologic testing was 57.8% (95% CI = 44.8 to 70.1) during the third week of follow-up and specificity was 98.4% (95% CI = 95.5 to 99.7). CONCLUSION: Rapid serologic testing is ineffective for detecting asymptomatic or non-severe cases of COVID-19 at early stages of the disease, but can be of value for surveillance of immunity response in primary care. The clinical profile and immune response of patients with COVID-19 in primary care differs from those in hospital-based populations.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".