The COVID-19 and TB syndemic: the way forward
Bibliographic record
Abstract
have killed approximately 5.7 million people worldwide over the past 2 years. The COVID-19 pandemic, and the non-pharmaceutical interventions to mitigate COVID-19 transmission (including social distancing regulations, partial lockdowns and quarantines), have disrupted healthcare services and led to a reallocation of resources to COVID-19 care. There has also been a tragic loss of healthcare workers who succumbed to the disease. This has had consequences for TB services, and the fear of contracting COVID-19 may also have contributed to reduced access to TB services. Altogether, this is projected to have resulted in a 5-year setback in terms of mortality from TB and a 9-year setback in terms of TB detection. In addition, past and present TB disease has been reported to increase both COVID-19 fatality and incidence. Similarly, COVID-19 may adversely affect TB outcomes. From a more positive perspective, the pandemic has also created opportunities to improve TB care. In this review, we highlight similarities and differences between these two infectious diseases, describe gaps in our knowledge and discuss solutions and priorities for future 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".