Prevalence of Depression, Anxiety, Delirium, and Post-Traumatic Stress Disorder Among COVID-19 Patients: Protocol for A Living Systematic Review
Bibliographic record
Abstract
Abstract BackgroundPrevious studies on the impact of 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 depression, anxiety, delirium, and post-traumatic stress disorder (PTSD) among COVID-19 patients.MethodsWe 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 utilize different tools to examine the bias risks (quality) regarding studies of varying design types, such as the revised Cochrane risk-of-bias tool (RoB 2) for randomized controlled trials (RCT), the Newcastle-Ottawa Scale (NOS) for cohort and case-control studies, etc. The literature searches would be updated every month. We will perform meta-analysis if any new eligible studies or data are obtained and resubmit an updated systematic review if any change in outcomes and heterogeneity is determined after the addition of the new studies. There will be no restrictions on language or year of publication.DiscussionThis LSR would provide an in-depth and up-to-date summary of the psychological impact of COVID-19 diagnosis and treatment on the patients. Systematic review registrationPROSPERO CRD42020196610
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 | 0.080 | 0.086 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.023 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.088 | 0.012 |
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".