Characteristics, management and factors associated with poor outcomes in COVID-19 patients in Burkina Faso: insights from a 2021 large-scale ambispective study
Bibliographic record
Abstract
Objectives: To assess treatment and identify predictive factors of worsening in COVID-19 patients. Methods: This study was ambispective (both prospective and retrospective) and part of a multidisciplinary, multicenter project designed to generate epidemiological, sociological and anthropological data about the COVID-19 epidemic in Burkina Faso. Medical records of patients admitted for COVID-19 at the hospitals of Ouagadougou and Bobo-Dioulasso from March 2020 to April 2021 were reviewed. To identify predictive factors of severe complications, we used Poisson regression models. Results: In total, 1,511 patients were included, of whom 70% were aged ≤50 years, 59% were men and 97% were living in an urban area. Of the 86% of patients treated, 92.9% of them received the combo Azithromycin-hydroxychloroquine. A total of 78 (5.2%) patients experienced complications during hospitalization, and 49 (3.3%) patients died. Multivariate analysis identified patient's age, residence and comorbidity as factors associated with poor outcomes. Conclusions: Although most people had symptoms, most of them recovered without sequelae, and few patients had severe forms of disease. Age was a strong predictor of worse outcomes in this population.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".