Insomnia in Long COVID: Prevalence, Neurological Associations, and Treatment Outcomes (Jan 2021– May 2025)
Bibliographic record
Abstract
This dataset supports a systematic review and meta-analysis examining the prevalence, neurological associations, and treatment outcomes of insomnia in individuals with Long COVID (Post-Acute Sequelae of SARS-CoV-2 infection, or PASC). The analysis includes peer-reviewed studies published between January 2021 and May 2025, retrieved from PubMed, Scopus, and Web of Science. The data files include: Study selection criteria and methodology summaries Extracted data on insomnia prevalence across demographic and clinical subgroups Associations with cognitive dysfunction, autonomic dysregulation, and neuroinflammatory markers Effect size estimates for interventions including Cognitive Behavioral Therapy for Insomnia (CBT-I), melatonin supplementation, acupuncture, and pharmacological agents All methods were conducted in accordance with PRISMA 2020 guidelines. Risk of bias was assessed using the Newcastle-Ottawa Scale and the Cochrane Risk of Bias tool. Meta-analyses were performed using random-effects models. This resource is intended to support clinicians, researchers, and public health professionals in understanding and treating insomnia as a persistent symptom of Long COVID.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.005 |
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".