Prevalence of delirium, depression, anxiety, and post-traumatic stress disorder among COVID-19 patients: protocol for a living systematic review
Bibliographic record
Abstract
BACKGROUND: Previous studies on the impact of corona virus disease 2019 (COVID-19) on the mental health of the patients has been limited by the lack of relevant data. With the rapid and sustained growth of the publications on COVID-19 research, we will perform a living systematic review (LSR) to provide comprehensive and continuously updated data to explore the prevalence of delirium, depression, anxiety, and post-traumatic stress disorder (PTSD) among COVID-19 patients. METHODS: We will perform a comprehensive search of the following databases: Cochrane Library, PubMed, Web of Science, EMBASE, and Chinese Biomedicine Literature to identify relevant studies. We will include peer-reviewed cross-sectional studies published in English and Chinese. Two reviewers will independently assess the methodological quality of included studies using the Joanna Briggs Institute Prevalence Critical Appraisal tool and perform data extraction. In the absence of clinical heterogeneity, the prevalence estimates with a 95% confidence interval (CI) of delirium, depression, anxiety, and post-traumatic stress disorder (PTSD) will be calculated by using random-effects model to minimize the effect of between-study heterogeneity separately. The literature searches will be updated every 3 months. We will perform meta-analysis if any new eligible studies or data are obtained. We will resubmit an updated review when there were relevant changes in the results, i.e., when outcomes became statistically significant (or not statistically significant anymore) or when heterogeneity became substantial (or not substantial anymore). DISCUSSION: This LSR will provide an in-depth and up-to-date summary of whether the common neuropsychiatric conditions observed in patients hospitalized for severe acute respiratory syndrome (SARS-CoV) and Middle East respiratory syndrome (MERS) are also prevalent in a different stage of COVID-19 patients. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020196610.
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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 | 0.077 | 0.085 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.025 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.070 | 0.011 |
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".