Epidemiological Trends for Cryptococcosis in Swaziland (Eswatini), Southern Africa
Bibliographic record
Abstract
Cryptococcosis is a fungal disease that is characterized by inflammation of the lungs and central nervous system, and it is commonly associated with HIV/AIDS. Even though the disease accounts for roughly 15% of all AIDS-related deaths, it is relatively neglected. This is most especially true in Southern Africa which has the highest HIV/AIDS cases in the world and accounts for more than 10% of all HIV/AIDS cases worldwide most especially in Southern African countries such as Swaziland (Eswatini) which has the highest HIV/AIDS adult prevalence rate in the world. Despite this, there are little or no previous studies with regards to the epidemiological trends for cryptococcosis in Swaziland (Eswatini) which further suggests that it is relatively neglected. With the increasing spread of virulent strains of the fungus such as Cryptococcus gattii causing outbreaks in several countries around the world, it is important to have a concrete understanding of the epidemiological trends for cryptococcosis in Swaziland (Eswatini). This is also important during the current coronavirus outbreak as previous studies have reported higher morbidity and mortality rates among COVID-19 patients that are also co-infected with HIV/AIDS, cryptococcus as well as other secondary infections. This is further supported by the fact that Southern Africa has the highest number of COVID-19 cases in Africa as well as one of the highest in the world. As a result, the purpose of this study is to determine the epidemiological trends for cryptococcosis in Swaziland (Eswatini) as this will enable adequate control, management, assessment, policies, and regulations that will be useful during outbreaks. This will be achieved by performing a repeated cross-sectional study to determine the epidemiological changes and trends for cryptococcosis in Swaziland (Eswatini) over a 5-year period from 2023 to 2028.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".