Risk factors for psychiatric symptoms in patients with long COVID: A systematic review
Bibliographic record
Abstract
Prolonged symptoms of COVID-19 have been found in many patients, often known as Long COVID. Psychiatric symptoms are commonly seen in Long COVID patients and could last for weeks, even months, after recovery. However, the symptoms and risk factors associated with it remain unclear. In the current systematic review, we provide an overview of psychiatric symptoms in Long COVID patients and risk factors associated with the development of those symptoms. Articles were systematically searched on SCOPUS, PubMed, and EMBASE up to October 2021. Studies involving adults and geriatric participants with a confirmed previous COVID-19 diagnosis and reported psychiatric symptoms that persist for more than four weeks after the initial infection were included. The risk of bias was assessed using the Newcastle-Ottawa Scale (NOS) for observational studies. Prevalence rates and risk factors associated with psychiatric symptoms were collected. This present study was registered at PROSPERO (CRD42021240776). In total, 23 studies were included. Several limitations in this review were the heterogeneity of studies' outcomes and designs, studies limited to articles published in English, and the psychiatric symptoms mainly were assessed using self-report questionnaires. The most prevalent reported psychiatric symptoms, from the most to the least reported, were anxiety, depression, post-traumatic stress disorder (PTSD), poor sleep qualities, somatic symptoms, and cognitive deficits. Being female and having previous psychiatric diagnoses were risk factors for the development of the reported symptoms.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".