The role of sleep and dreams in long‐<scp>COVID</scp>
Bibliographic record
Abstract
Recent investigations show that many people affected by SARS-CoV2 (COVID-19) report persistent symptoms 2-3 months from the onset of the infection. Here, we report the Italian findings from the second International COVID-19 Sleep Study survey, aiming to investigate sleep and dream alterations in participants with post-acute symptoms, and identify the best determinants of these alterations among patients with long-COVID. Data from 383 participants who have had COVID-19 were collected through a web-survey (May-November 2021). Descriptive analyses were performed to outline the sociodemographic characteristics of long-COVID (N = 270, with at least two long-lasting symptoms) and short-COVID (N = 113, with none or one long-lasting symptom) participants. They were then compared concerning sleep and dream measures. We performed multiple linear regressions considering as dependent variables sleep and dream parameters discriminating the long-COVID group. Age, gender, work status, financial burden, COVID-19 severity and the level of care were significantly different between long-COVID and short-COVID subjects. The long-COVID group showed greater sleep alterations (sleep quality, daytime sleepiness, sleep inertia, naps, insomnia, sleep apnea, nightmares) compared with the short-COVID group. We also found that the number of long-COVID symptoms, psychological factors and age were the best explanatory variables of sleep and oneiric alterations. Our findings highlight that sleep alterations are part of the clinical presentation of the long-COVID syndrome. Moreover, psychological status and the number of post-acute symptoms should be considered as state-like variables modulating the sleep problems in long-COVID individuals. Finally, according to previous investigations, oneiric alterations are confirmed as a reliable mental health index.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".