Global trends and gaps in research related to latent tuberculosis infection
Bibliographic record
Abstract
BACKGROUND: There is a global commitment to eliminating tuberculosis (TB). It is critical to detect and treat cases of latent TB infection (LTBI), the reservoir of new TB cases. Our study assesses trends in publication of LTBI-related research. METHODS: We used the keywords ("latent tuberculosis" OR "LTBI" OR "latent TB") to search the Web of Science for LTBI-related articles published 1995-2018, then classified the results into three research areas: laboratory sciences, clinical research, and public health. We calculated the proportions of LTBI-related articles in each area to three areas combined, the average rates of LTBI-related to all scientific and TB-related articles, and the average annual percent changes (AAPC) in rates for all countries and for the top 13 countries individually and combined publishing LTBI research. RESULTS: The proportion of LTBI-related articles increased over time in all research areas, with the highest AAPC in laboratory (38.2%/yr), followed by public health (22.9%/yr) and clinical (15.1%/yr). South Africa (rate ratio [RR] = 8.28, 95% CI 5.68 to 12.08) and India (RR = 2.53, 95% CI 1.74 to 3.69) had higher RRs of overall TB-related articles to all articles, but did not outperform the average of the top 13 countries in the RRs of LTBI-related articles to TB-related articles. Italy (RR = 1.95, 95% CI 1.45 to 2.63), Canada (RR = 1.73, 95% CI 1.28 to 2.34), and Spain (RR = 1.53, 95% CI 1.13 to 2.07) had higher RRs of LTBI-related articles to TB-related articles. CONCLUSIONS: High TB burden countries (TB incidence > 100 per 100,000 population) published more overall TB-related research, whereas low TB burden countries showed greater focus on LTBI. Given the potential benefits, high TB burden countries should consider increasing their emphasis on LTBI-related research.
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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 | 0.034 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.034 | 0.054 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".