Median duration and factors that influence the duration of symptom resolution in COVID‐19 patients in Ethiopia: A follow‐up study involving symptomatic cases
Bibliographic record
Abstract
Abstract Background Understanding the clinical features of COVID‐19 and duration for resolution of symptoms is crucial for isolation of patients and tailoring public health messaging, interventions and policy. Therefore, this study aims to assess the median duration of COVID‐19 signs and symptoms’ resolution and explore its predictors among symptomatic COVID‐19 patients in Ethiopia. Methods A hospital‐based prospective cohort study involving 124 COVID‐19 cases was conducted at Eka Kotebe General Hospital, COVID‐19 Isolation and Treatment Center. The study participants were all symptomatic COVID‐19 adult patients admitted to the hospital from 18 March to 20 August 2020. Physicians at the centre recorded the data using a log sheet. Cox proportional‐hazards regression model was conducted. Statistical significance was defined at P < 0.05. Results A total of 124 symptomatic COVID‐19 patients with a mean age of 42 years (±17) were involved in the study. The median duration of symptom resolution of COVID‐19 was seven days with a minimum of two and a maximum of sixty‐eight days. Sex and body mass index (BMI) were statistically significant predictors of the symptom resolution. The hazard of having delayed sign or symptom resolution in males was 55% higher than in females (P = 0.039; CI: 0.22–0.96) and the hazard of delayed sign or symptom resolution in those with BMI ≥ 25 kg/m2 was 35% higher than in those with BMI < 25 kg/m2 (P = 0.041; CI: 0.44–0.98]). Conclusions The median duration of COVID‐19 symptom resolution was seven days. Being male and/or having a BMI ≥ 25 kg/m2 were predictors of a delayed sign or symptom resolution time. Therefore, it is important to consider proportion of males and those with BMI ≥ 25 kg/m2 when preparing isolation and treatment centres. Males and individuals with BMI ≥ 25 kg/m2 shall also be given priority when shielding from the COVID‐19.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".