Effect of Preinfection Health Status on COVID‐19 Severity and Cognitive Function
Bibliographic record
Abstract
BACKGROUND: This observational cohort study investigates how infection health factors influence COVID-19 severity and cognitive outcomes. We collected preinfection data from hospitalized COVID-19 patients, including demographic information and baseline health conditions prior to diagnosis, and examined their associations with hospitalization duration and cognitive function assessed after infection. METHODS: Data were obtained from Hui Ya Hospital, The First Affiliated Hospital, Sun Yat-sen University, China. The study included confirmed COVID-19 patients requiring hospitalization. Among the 147 collected cases, two were excluded due to missing data, leaving a final sample of 145 patients. The Montreal Cognitive Assessment (MoCA), which evaluates global cognitive function with a total score of 0-30, was used to assess cognitive function, while hospitalization duration and routine clinical examinations were analyzed as indicators of disease severity. Additionally, the SF-12v2 score reflecting Health-Related Quality of Life was used to evaluate patients' overall health status. Statistical analyses were conducted to identify preinfection factors associated with COVID-19 outcomes. RESULTS: Preinfection baseline health status was significantly correlated with both hospitalization duration (p < 0.0001, 95% CI [-0.47, -0.16]) and MoCA scores (p = 0.0001, 95% CI [0.15, 0.46]). Patients with better preinfection health conditions experienced shorter hospital stays and demonstrated better cognitive function postinfection. CONCLUSION: Our findings indicate that preinfection baseline health conditions play a crucial role in determining both the severity of COVID-19 and postinfection cognitive function. Specifically, impairments were more pronounced in the visuospatial, naming, attention, calculation, language, and memory domains. Additionally, our results suggest a potential link between COVID-19 outcomes and patients' preexisting underlying diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".