Recent advances in the diagnosis and management of typhoid fever in Africa: A review
Bibliographic record
Abstract
Typhoid fever, a classical disease of enteric origin caused by Salmonella species of bacteria, is among the most important diseases threatening public health in Africa. The African continent is a marker for both low resources within the healthcare system and poor disease control policy formulations in managing endemic infectious diseases. Since the colonial era, the Widal serological test has been used to confirm typhoid fever in Africa, however recent studies recommend blood culture, and when blood culture cannot be obtained, clinical findings, laboratory Widal test confirmation, and ruling out other febrile illnesses as confirmatory pathway to diagnose typhoid fever in Africa. Managing typhoid fever relies on antimicrobials. In 1980s chloramphenicol was the medication of choice. Years later, amoxicillin and co-trimoxazole were adopted. However, the instantaneous rise of resistant strains of Salmonella enterica confers an important challenge to treat the burdensome enteric fever. The current treatment algorithm of typhoid fever in Africa relies significantly on the use of fluoroquinolones, macrolides, and cephalosporins. Developed nations have successfully addressed and controlled typhoid fever via improvement in accessing safe water and food, better sanitary and hygienic behaviours, and vaccines development. Nevertheless, there is significant evidence to infer improvement in the diagnosis management of typhoid fever over the last few decades, and efforts are underway to control the disease spread in Africa. This review aims to provide an overview of the latest developments in typhoid fever diagnosis and management in Africa and provide key recommendations for a coordinated approach to mitigate typhoid in the continent.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".