The Effect of Symptoms on the Survival Time of Coronavirus Patients in the Sudanese Population
Bibliographic record
Abstract
The COVID-19 pandemic has rapidly spread worldwide, resulting in substantial rates of illness and death. Gaining insight into the various factors that impact the duration of survival among individuals diagnosed with COVID-19 is of utmost importance to inform clinical practices and public health strategies This study aims to evaluate the relationship between the acuteness of symptoms and the survival time of coronavirus patients in Sudan. The Kaplan-Meier curves and log-rank test were used to determine the symptom pattern. The results of COVID-19 and Cox regression were utilized to determine the most critical symptoms affecting coronavirus patients. The log-rank test revealed that there are differences in the pattern of age and symptoms among coronavirus patients. Cox regression revealed that symptoms affect on the survival time of coronavirus patients. The Cox proportional Hazard Model shows that the hazard of age at any time increases by 116.5%, diarrhea increases by 9%, headache increases by 62.0%, fatigability increases by 13.3%, and other symptoms increase by 47.3%. This study differs from prior studies in several ways. No current study in Sudan has used survival analysis to discover the most relevant symptoms affecting survival time.
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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 | 0.017 | 0.059 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".