Translating the Tuberculosis Research Agenda: Much Accomplished, but Much More to Be Done: Figure 1.
Bibliographic record
Abstract
Despite the availability of effective diagnostics and curative treatment regimens for tuberculosis, millions of people die each year of this disease. The steady global increase in the number of tuberculosis cases caused by multidrug-resistant and extensively drug-resistant strains of Mycobacterium tuberculosis are of major concern, especially in light of the thin tuberculosis drug pipeline. New tuberculosis drugs are undergoing clinical evaluation, and renewed hope comes from fresh approaches to improve treatment outcomes using a range of adjunct host-directed cellular and repurposed drug therapies. Current efforts in developing second-generation and new rapid point-of-care diagnostic assays take advantage of recent genetic and molecular advances. Slow progress in the development of prophylactic and therapeutic vaccines requires increased funding for basic as well as translational research. Although major challenges remain, these can be overcome by cementing our resolve, raising advocacy, bolstering global funder investments, and leveraging more effective collaborations through equitable public-private partnerships.
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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.061 | 0.046 |
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".