ScreenTB: a tool for prioritising risk groups and selecting algorithms for screening for active tuberculosis
Bibliographic record
Abstract
SETTING AND OBJECTIVES: There is an urgent need to improve tuberculosis (TB) case detection globally. This would require greater focus on the implementation of TB screening programs. However, to be productive, cost-effective, and ethical, TB screening efforts should be tailored to their local context, targeted to the populations most likely to benefit and utilizing diagnostic tools with sufficient accuracy.DESIGN AND RESULTS: We have developed an online tool, ScreenTB to help National TB Programmes (NTPs) and their partners plan TB screening activities by modeling the potential outcomes of screening programs, including yield of TB cases diagnosed (true- and false-positives), costs, and cost-effectiveness, specific to the populations screened and the diagnostic algorithms used. In Myanmar, ScreenTB was used to assist the NTP in prioritizing risk groups for screening efforts and selecting appropriate screening algorithms to maximize case detection and minimize false-positive diagnoses.CONCLUSION: The ScreenTB tool can help facilitate the prioritization of risk groups for screening and the selection of appropriate screening algorithms. This is useful when used as part of a larger planning process that considers feasibility of screening, vulnerability of risk groups, potential impact of screening on TB transmission, human rights implications of screening and equity in health care access.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".