Time to recovery and its predictors among adults hospitalized with COVID-19: A prospective cohort study in Ethiopia
Bibliographic record
Abstract
BACKGROUND: Various factors may determine the duration of viral shedding (the time from infection to viral RNA-negative conversion or recovery) in COVID-19 patients. Understanding the average duration of recovery and its predictors is crucial in formulating preventive measures and optimizing treatment options. Therefore, evidence showing the duration of recovery from COVID-19 in different contexts and settings is necessary for tailoring appropriate treatment and prevention measures. This study aimed to investigate the average duration and the predictors of recovery from Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection among COVID-19 patients. METHOD: A hospital-based prospective cohort study was conducted at Eka Kotebe General Hospital, COVID-19 Isolation and Treatment Center from March 18 to June 27, 2020. The Center was the first hospital designated to manage COVID-19 cases in Ethiopia. The study participants were all COVID-19 adult patients who were admitted to the center during the study period. Follow up was done for the participants from the first date of diagnosis to the date of recovery (negative Real-time Reverse Transcriptase Polymerase Chain Reaction (rRT-PCT) test of throat swab). RESULT: A total of 306 COVID-19 cases were followed up to observe the duration of viral clearance by rRT-PCR. Participants' mean age was 34 years (18-84 years) and 69% were male. The median duration of viral clearance from each participant's body was 19 days, but the range was wide: 2 to 71 days. Cough followed by headache was the leading sign of illness among the 67 symptomatic COVID-19 patients; and nearly half of those with comorbidities were known cancer and HIV/AIDS patients on clinical follow up. The median duration of recovery from COVID-19 was different for those with and without previous medical conditions or comorbidities. The rate of recovery from SARS-CoV-2 infection was 36% higher in males than in females (p = 0.043, CI: 1.01, 1.85). The rate of recovery was 93% higher in those with at least one comorbidity than in those without any comorbidity. The risk of delayed recovery was not influenced by blood type, BMI and presence of signs or symptoms. The findings showed that study participants without comorbidities recovered more quickly than those with at least one comorbidity. Therefore, isolation and treatment centers should be prepared to manage the delayed stay of patients having comorbidity.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".