New and improved tuberculosis diagnostics: evidence, policy, practice, and impact
Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim is to summarize the evidence base for tuberculosis (TB) diagnostics, review recent policies on TB diagnostics, and discuss issues such as how evidence is translated into policy, limitations of the existing evidence base, and challenges involved in translating policies into impact. RECENT FINDINGS: Case detection continues to be a major obstacle to global TB control. Fortunately, due to an unprecedented level of interest, funding, and activity, the new diagnostics pipeline for TB has rapidly expanded. There have been several new policies and guidelines on TB diagnostics. However, there are major gaps in the existing pipeline (e.g. lack of a point-of-care test) and the evidence base is predominantly made up of research studies of test accuracy. SUMMARY: With the availability of new diagnostics and supporting policies, the next major step is translation of policy into practice. The impact of new tests will depend largely on the extent of their introduction and acceptance into the global public sector. This will itself depend in part on policy decisions by international technical agencies and national TB programs. With the engagement of all key stakeholders, we will need to translate evidence-based policies into epidemiological and public health impact.
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 | 0.019 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".