Influence of Preexisting Psychotic and Bipolar Disorders on the Outcomes of Acutely Hospitalized Patients With COVID-19
Bibliographic record
Abstract
OBJECTIVE: COVID-19 outcomes are often worse among patients with preexisting conditions. The authors assessed the impact of preexisting psychotic and bipolar disorders (PsBPs) on COVID-19 outcomes. METHODS: A retrospective cohort study was conducted with COVID-19 patients admitted to three medical centers between April 20, 2020, and October 20, 2023. Patients were grouped into individuals with PsBPs (i.e., schizophrenia, other psychotic disorders, and bipolar disorders) and those with no psychiatric disorders (NPDs), defined as individuals without preexisting conditions such as nonpsychotic depression or anxiety. Data on demographic characteristics, clinical features, and COVID-19 severity were collected. The primary outcome was COVID-19 in-hospital mortality rate, and the secondary outcome was the association of PsBPs with COVID-19 severity. RESULTS: Among 7,370 hospitalized COVID-19 patients (43.7% female; mean age=42.7 years), 12.2% had a PsBP. Patients with PsBPs had higher intensive care unit (ICU) admission rates (44.5% vs. 21.8%, p=0.003) and mortality rates (39.2% vs. 23.8%, p=0.045). Time intervals to ICU admission (7.3 vs. 8.2 days, p=0.001) and in-hospital death (8.0 vs. 12.2 days, p=0.001) were significantly shorter in the PsBP group, compared with the NPD group. CONCLUSIONS: COVID-19 patients with PsBPs had worse outcomes, including higher ICU admission and mortality rates, and greater disease severity. These findings highlight the importance of early detection and tailored interventions for this vulnerable population.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".