Characteristics of COVID-19 patients and risk factors of mortality in the early times of pandemic, Herat-Afghanistan
Bibliographic record
Abstract
Objective: Coronaviruses are a large family of viruses that cause different types of diseases. This study aims to evaluate the risk factors for mortality based on comorbidity and socio-demographic characteristics among COVID-19 patients. 
 
 Methods: This cross-sectional study conducted in Herat, Afghanistan, from February 24 to July 5, 2020, used data provided by the public health department, including socio-demographics, symptoms, comorbidities, hospitalization, contact history, and COVID-19 test type. The Chi-square test was used to observe differences between categorical variables. In bivariate analysis, all independent variables with a significant p-value were put into the model. Odds ratios and 95% confidence intervals were calculated, and a p-value less than 0.05 was considered statistically significant. 
 
 Results: The study analyzed 11,183 COVID-19 cases, with a 53.5% positivity rate. Recovery rates in the city and Herat province districts were 96.2% and 94.7%, respectively. Case-fatality rates varied with age, with 0.4% for those aged 1-29 and 33% for those aged 80-105. Mortality rates were highest for those with COPD and cancer, at 12.5% and 18.2%, respectively. In the logistic regression results, age, gender, and COPD were significant variables for COVID-19 mortality. 
 
 Conclusion: By providing more health service facilities to people in risk groups, especially in rural areas, the mortality rate of COVID-19 and other diseases can be decreased.
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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) | 0.001 | 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".