Recommendations on modern contact investigation methods for enhancing tuberculosis control [Review article]
Bibliographic record
Abstract
Effective contact investigations are paramount to the success of tuberculosis (TB) control in high-risk communities in low TB prevalence countries. National and international guidelines on TB contact investigations are available and vary widely on recommendations. Because of the limitations of traditional contact tracing, new approaches are under investigation, and in some cases in use, to ensure effective TB control in those persons and communities at greatest risk. These non-traditional approaches include the use of social network analysis, geographic information systems and genomics, in addition to the widespread use of genotyping, to better understand TB transmission. Detailed guidelines for the use of these methods during TB outbreaks and in routine follow-up of TB contact investigations do not currently exist despite evidence that they may improve TB control efforts. It remains unclear as to when it is most appropriate and effective to use a network-informed approach alone, or in combination with other methodologies as well as the extent of data collection required to inform practice. TB controllers should consider developing the capacity to facilitate the systematic collection, analysis, and interpretation of contact investigation data using such novel methodologies, particularly in high-risk communities. Further investigation should focus on questionnaire development and adaptation, electronic data management and infrastructure, development of local capability and consultant expertise, and the use of coordinated approaches, including deployment strategies and evaluation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".