Sleep symptoms are essential features of long‐<scp>COVID</scp>– Comparing healthy controls with<scp>COVID</scp>‐19 cases of different severity in the international<scp>COVID</scp>sleep study (<scp>ICOSS‐II</scp>)
Bibliographic record
Abstract
Many people report suffering from post-acute sequelae of COVID-19 or "long-COVID", but there are still open questions on what actually constitutes long-COVID and how prevalent it is. The current definition of post-acute sequelae of COVID-19 is based on voting using the Delphi-method by the WHO post-COVID-19 working group. It emphasizes long-lasting fatigue, shortness of breath and cognitive dysfunction as the core symptoms of post-acute sequelae of COVID-19. In this international survey study consisting of 13,628 subjects aged 18-99 years from 16 countries of Asia, Europe, North America and South America (May-Dec 2021), we show that post-acute sequelae of COVID-19 symptoms were more prevalent amongst the more severe COVID-19 cases, i.e. those requiring hospitalisation for COVID-19. We also found that long-lasting sleep symptoms are at the core of post-acute sequelae of COVID-19 and associate with the COVID-19 severity when COVID-19 cases are compared with COVID-negative cases. Specifically, fatigue (61.3%), insomnia symptoms (49.6%) and excessive daytime sleepiness (35.8%) were highly prevalent amongst respondents reporting long-lasting symptoms after hospitalisation for COVID-19. Understanding the importance of sleep-related symptoms in post-acute sequelae of COVID-19 has a clinical relevance when diagnosing and treating long-COVID.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".