Prevalence and predictors of persistent post-COVID-19 symptoms
Bibliographic record
Abstract
BACKGROUND: The pandemic has affected hundreds of millions of people; early reports suggesting high rates of prolonged symptoms may be prone to selection bias. METHODS: In a program caring for all SARS-CoV-2 positive inpatients and outpatients between March to October 2020, and offering universal 90-day follow-up, we compared those who died prior to 90 days, not responding to follow-up, declining, or accepting follow-up. Among those seen or declining follow-up, we determined the prevalence and predictors of persistent symptoms. RESULTS: Among 993 patients, 21 (2.1%) died prior to 90 days, 506 (50.9%) did not respond, 260 (26.1%) declined follow-up because they were well, and 206 (20.7%) were fully assessed. Of 466 who responded to follow-up inquiry, 133 (28.5%) reported ≥1 persistent symptom, including constitutional (15.5%), psychiatric (14.2%), rheumatologic (13.1%), neurologic (13.1%), cardiorespiratory (12.0%), and gastrointestinal (1.7%). Predictors differed for each symptom type. Any persistent symptom was more common in older patients (adjusted odds ratio [aOR] 1.11, 95% CI 1.04 to 1.18/5 years), those diagnosed in hospital (aOR 2.03, 95% CI 1.24 to 3.33) and those with initial constitutional and rheumatologic symptoms. Patients not responding to follow-up were younger and healthier at baseline. CONCLUSION: Persistent symptoms are common and diverse 3 months post-COVID-19 but are likely over-estimated by most reports.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".