Typhoid Fever: Way Forward
Bibliographic record
Abstract
The Tackling Typhoid supplement shows that typhoid fever continues to be a problem globally despite socioeconomic gains in certain settings. Morbidity remains high in many endemic countries, notably in sub-Saharan Africa and South Asia. In addition, antimicrobial resistance is a growing issue that poses a challenge for clinical management. The findings from this supplement revealed that outside of high-income countries, there were few reliable population-based estimates of typhoid and paratyphoid fever derived from surveillance systems. This indicates the need for monitoring systems that can also characterize the effectiveness of interventions, particularly in low- and middle-income settings. The country case studies indicated that gains in economic conditions, education, and environmental health may be associated with reductions in typhoid fever burden. Over the study period, the effect is mainly notable in countries with higher baseline levels of economic development, female literacy, and investments in public sanitation. High burden countries must continue to invest in strategies at the local level to address environmental factors such as access to safe drinking water and improved public sanitation that are known to interrupt transmission or diminish the risk of acquiring typhoid. Developing more effective vaccines and incorporating appropriate immunization strategies that target populations with the greatest risk could potentially alleviate disease burden.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".