Prognostic factors and outcomes of COVID-19 cases in Ethiopia: multi-center cohort study protocol
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2) and became pandemic after emerging in Wuhan, China, in December 2019. Several studies have been conducted to understand the key features of COVID-19 and its public health impact. However, the prognostic factors of COVID-19 are not well studied in the African setting. In this study, we aim to determine the epidemiological and clinical features of COVID-19 cases, immunological and virological courses, interaction with nutritional status, and response to treatment for COVID-19 patients in Ethiopia. METHODS: A multi-center cohort study design will be performed. Patients with confirmed COVID-19 infection admitted to selected treatment centers will be enrolled irrespective of their symptoms and followed-up for 12 months. Baseline epidemiological, clinical, laboratory and imaging data will be collected from treatment records, interviews, physical measurements, and biological samples. Follow-up data collection involves treatment and prognostic outcomes to be measured using different biomarkers and clinical parameters. Data collection will be done electronically using the Open Data Kit (ODK) software package and then exported to STATA/SPSS for analysis. Both descriptive and multivariable analyses will be performed to assess the independent determinants of the treatment outcome and prognosis to generate relevant information for informed prevention and case management. The primary outcomes of this study are death/survival and viral shedding. Secondary outcomes include epidemiological characteristics, clinical features, genetic frequency shifts (genotypic variations), and nutritional status. DISCUSSION: This is the first large prospective cohort study of patients in hospitals with COVID-19 in Ethiopia. The results will enable us to better understand the epidemiology of SARS-CoV-2 in Africa. This study will also provide useful information for effective public health measures and future pandemic preparedness and in response to outbreaks. It will also support policymakers in managing the epidemic based on scientific evidence. TRIAL REGISTRATION: The Protocol prospectively registered in ClinicalTrials.gov (NCT04584424) on 30 October, 2020.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".