New tuberculosis technologies: challenges for retooling and scale-up [State of the art series. New tools. Number 4 in the series]
Bibliographic record
Abstract
The availability of new tools does not mean that they will be adopted, used correctly, scaled up or have public health impact. Experience to date with new diagnostics suggests that many national tuberculosis programmes (NTPs) in high-burden countries are reluctant to adopt and scale up new tools, even when these are backed by evidence and global policy recommendations. We suggest that there are several common barriers to effective national adoption and scale-up of new technologies: global policy recommendations that do not provide sufficient information for scale-up, complex decision-making processes and weak political commitment at the country level, limited engagement of and support to NTP managers, high cost of tools and poor fit with user needs, unregulated markets and inadequate business models, limited capacity for laboratory strengthening and implementation research, and insufficient advocacy and donor support. Overcoming these barriers will require enhanced country-level advocacy, resources, technical assistance and political commitment. Some of the BRICS (Brazil, Russia, India, China, South Africa) countries are emerging as early adopters of policies and technologies, and are increasing their investments in TB control. They may provide the first opportunities to fully assess the public health impact of new tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".