Introductory Article on Global Burden and Epidemiology of Typhoid Fever
Bibliographic record
Abstract
and study national and regional typhoid fever trends. In addition to a global systematic review, eight case studies were undertaken to examine typhoid and paratyphoid fever trends in endemic countries alongside changes in relevant contextual factors. Incidence variations exist both within and between regions with large subnational differences as well, suggesting that public health changes impacting typhoid and paratyphoid fevers in one setting may not have similar impacts in another. This supplement also brings to light the lack of national typhoid fever surveillance systems, inconsistencies in diagnostics, and the lack of typhoid fever associated morbidity and mortality data in many countries, making it difficult to accurately quantify and track burden of disease. To better understand typhoid fever there is a need for more high-quality data from resource-poor settings. The implementation of typhoid surveillance systems alongside the transition to blood-culture confirmation of cases, where possible, would aid in the improvement of data quality in low-income settings. The following supplement includes the results of our global systematic review, eight-country case study articles, a qualitative article informed by semistructured interviews, and a conclusion article on potential ways forward for typhoid control.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.006 |
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".