Post-COVID-19 condition symptoms among emergency department patients tested for SARS-CoV-2 infection
Bibliographic record
Abstract
Symptoms of the Post-COVID-19 Condition are often non-specific making it a challenge to distinguish them from symptoms due to other medical conditions. In this study, we compare the proportion of emergency department patients who developed symptoms consistent with the World Health Organization’s Post-COVID-19 Condition clinical case definition between those who tested positive for Severe Acute Respiratory Syndrome Coronavirus-2 infection and time-matched patients who tested negative. Our results show that over one-third of emergency department patients with a proven acute infection meet Post-COVID-19 Condition criteria 3 months post-index visit. However, one in five test-negative patients who claim never having been infected also report symptoms consistent with Post-COVID-19 Condition highlighting the lack of specificity of the clinical case definition. Testing for SARS-CoV-2 during the acute phase of a suspected infection should continue until specific biomarkers of Post-COVID-19 Condition become available for diagnosis and treatment. In this study of more than 6500 emergency department patients, symptoms consistent with Long COVID are reported by 38.9% of SARS-CoV-2 test-positive and 20.7% of test-negative patients three months after their visit. A documented SARS-CoV-2 infection increased the risk fourfold of reporting Long COVID symptoms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".