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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".