Apocalypse or redemption: responding to extensively drug-resistant tuberculosis
Bibliographic record
Abstract
Responding to drug-resistant tuberculosis is possibly one of the most profound challenges facing global health. Leading experts have used apocalyptic language in describing the scope of the challenges posed by extensively drug-resistant TB (XDR-TB), even suggesting that we resort to prayer as a solution.1,2 Recent reports indicating that aggressive treatment confers benefit raise hope that the situation may not be so dire.3 However, the structural and political changes and resources needed to prevent and treat XDR-TB on a large scale are not sufficient to assure that the tide of XDR-TB will be stemmed any time soon. Drug-resistant TB is not the result of catastrophic natural forces such as earthquakes, tsunamis and hurricanes. It is not caused by malign human intent, as are terrorism and war, nor is it fostered by our dysfunctional relationship with the animal kingdom as are severe acute respiratory syndrome (SARS) and avian influenza. The locus of risk and control is entirely within the human domain. Our response to the emergence of drug-resistant TB is profoundly ethical as it raises issues of how justice and human rights are realized in our collective response to a disease. It also underscores how the global community responds to its most disadvantaged members. The progressive worsening of resistance of TB to pharmacotherapy has raised the spectre of a response to TB without medication – what some have labelled the dawn of the post-antibiotic age. The combination of high rates of TB infection with high seropositivity rates for HIV in sub-Saharan Africa adds new levels of complexity to diagnosis and treatment and has raised the ante of global TB control.4 WHO has launched an eight-point plan to respond to XDR-TB.5 This paper provides an elaboration of these recommendations and adds some additional considerations as moral correlates to the current WHO plan (Box 1). Box 1The WHO eight-point plan and additional considerations WHO recommendations 1. Strengthen quality of basic TB and HIV/AIDS control 2. Scale up programmatic management of MDR-TB and XDR-TB 3. Strengthen laboratory services 4. Expand MDR-TB and XDR-TB surveillance 5. Develop and implement infection control measures 6. Strengthen advocacy, communication and social mobilization 7. Pursue resource mobilization at all levels 8. Promote research and development of new tools Additional considerations 1. Adherence research 2. Building the evidence-base for infection control practices 3. Supporting communities 4. Enhancing public health response while addressing the social determinants of health 5. Embracing palliative care 6. Advocacy for research
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".