Symptoms compatible with long-COVID in healthcare workers with and without SARS-CoV-2 infection – results of a prospective multicenter cohort
Bibliographic record
Abstract
Abstract Background The burden of long-term symptoms (i.e. long-COVID) in patients after mild COVID-19 is debated. Within a cohort of healthcare workers (HCW), frequency and risk factors for symptoms compatible with long-COVID are assessed. Methods Participants answered baseline (August/September 2020) and weekly questionnaires on SARS-CoV-2 nasopharyngeal swab (NPS) results and acute disease symptoms. In January 2021, SARS-CoV-2 serology was performed; in March, symptoms compatible with long-COVID (including psychometric scores) were asked and compared between HCW with positive NPS, seropositive HCW without positive NPS (presumable a-/pauci-symptomatic infections), and negative controls. Also, the effect of time since diagnosis and quantitative anti-S was evaluated. Poisson regression was used to identify risk factors for symptom occurrence. Results Of 3’334 HCW (median 41 years; 80% female), 556 (17%) had a positive NPS and 228 (7%) were only seropositive. HCW with positive NPS more frequently reported ≥1 symptom compared to controls (73% vs .52%, p<0.001); seropositive HCW without positive NPS did not score higher than controls (58%vs.52%, p=0.13), although impaired taste/olfaction (16% vs .6%, p<0.001) and hair loss (17% vs .10%, p=0.004) were more common. Exhaustion/burnout was reported by 24% of negative controls. Many symptoms remained elevated in those diagnosed >6 months ago; anti-S titers correlated with high symptom scores. Acute viral symptoms in weekly questionnaires best predicted long-COVID symptoms. Physical activity at baseline was negatively associated with neurocognitive impairment and fatigue scores. Conclusions Seropositive HCW without positive NPS are only mildly affected by long-COVID. Exhaustion/burnout is common, even in non-infected HCW. Physical activity might be protective against neurocognitive impairment/fatigue symptoms after COVID-19. summary In this prospective healthcare worker cohort, participants with SARS-CoV-2-positive nasopharyngeal swab were most likely to report long-COVID symptoms, whereas seropositive participants without positive swab were only mildly affected. Physical activity at baseline was negatively associated with neurocognitive impairment and fatigue.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 0.001 |
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".