Commentary: Lessons from the COVID-19 global health response to inform TB case finding
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) has emerged as a serious threat to global public health, demanding urgent action and causing unprecedented worldwide change in a short space of time. This disease has devastated economies, infringed on individual freedoms, and taken an unprecedented toll on healthcare systems worldwide. As of 1 April 2020, over a million cases of COVID-19 have been reported in 204 countries and territories, resulting in more than 51,000 deaths. Yet, against the backdrop of the COVID-19 pandemic, lies an older, insidious disease with a much greater mortality. Tuberculosis (TB) is the leading cause of death by a single infectious agent and remains a potent threat to millions of people around the world. We discuss the differences between the two pandemics at present, consider the potential impact of COVID-19 on TB case management, and explore the opportunities that the COVID-19 response presents for advancing TB prevention and control now and in future.
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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 | 0.016 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.072 | 0.063 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".