Number of Pre-Existing Comorbidities and Prognosis of COVID-19: A Retrospective Cohort Study
Bibliographic record
Abstract
Abstract Background: Though many studies have described the association of coronavirus disease 2019 (COVID-19) and different kinds of noncommunicable chronic diseases, information with the combine effects of comorbidities to COVID-19 patients have not been well characterized yet. The aim of this study was to examine the associations of numbers of comorbidities with critical type and death of COVID-19. Methods: This was a single-centered retrospective study among patients with COVID-19. All patients with COVID-19 enrolled in this study were diagnosed according to World Health Organization interim guidance. Six different kinds of noncommunicable chronic diseases were included in this study. The logistic regression model was used to estimate the fixed effect of numbers of comorbidities on critical type or death, adjusting for potential confounders. Results: In total, 475 COVID-19 patients were enrolled in our study, included 234 females and 241 males. Hypertension was the most frequent type (162 [34.1%] of 475 patients). Patients with two or more comorbidities have higher risk of critical type (OR 3.072, 95% CI [1.581, 5.970], p=0.001) and death (OR 5.538, 95% CI [1.577, 19.451], p=0.008) compared to patients without comorbidities. And the results were similar after adjusting for age and gender in critical type (OR 2.021, 95% CI [1.002–4.077], p=0.049) and death (OR 3.653, 95% CI [0.989, 13.494], p=0.052). Conclusions: The number of comorbidities was an independent risk factor for critical type and death in COVID-19 patients.
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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 | 0.043 | 0.390 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.001 | 0.012 |
| 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; both teacher heads agree on what is shown here.
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".