XDR typhoid in Pakistan: A threat to global health security and a wake-up call for antimicrobial stewardship
Bibliographic record
Abstract
Extensively drug-resistant (XDR) typhoid, caused by Salmonella enterica serotype Typhi, has emerged as a critical global health security threat, with Pakistan, particularly Sindh province, at its epicenter. The misuse of antibiotics, inadequate diagnostic tools, and poor water and sanitation infrastructure have created ideal conditions for the rise of antimicrobial resistance (AMR). XDR typhoid strains resistant to multiple first-line antibiotics have been linked to environmental contamination, with urban areas like Karachi demonstrating high rates of waterborne transmission. International travel has amplified this threat, exporting cases to countries including the United States, the United Kingdom, and Canada, thus highlighting its global implications. This commentary examines the historical context of typhoid treatment, the drivers of AMR in Pakistan, and the critical role of antimicrobial stewardship in combating XDR typhoid. It advocates for an integrated approach that would encompass improvements in water quality, expanded vaccination coverage with typhoid conjugate vaccines (TCVs), and stringent audit of antibiotic prescription practices. Immediate local and global action is needed to contain this public health crisis and prevent the resurgence of typhoid as a largely untreatable disease. This situation underscores the urgency of addressing AMR to safeguard global health security.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".