Obesity and Prevalence of Latent Tuberculosis: A Population-Based Survey
Bibliographic record
Abstract
Objective: Diminution in body weight is a known risk factor that increases the burden of active tuberculosis (TB). However, conflicting evidence exists for the effect of body weight on the risk of latent tuberculosis infection (LTBI). The objective of the present study is to examine the prevalence of LTBI at different body weights, evaluate the extent of association between body mass index (BMI) and LTBI and identify factors mediating this relationship in an adult population. Methods: We conducted a cross-sectional study to estimate the relationship between BMI and LTBI in participants from the US-National Health and Nutrition Examination Survey (NHANES; 2012, n = 5156; 514 with LTBI and 4642 controls). Results: The association between BMI and levels of cardiometabolic risk markers in both LTBI and control groups had a similar profile. When adjusted for age and sex, BMI was significantly inversely correlated with the prevalence of LTBI ( r = −0.147, P < .001). Effect of BMI on the risk of LTBI was evaluated using multivariate logistic regression models adjusted for age, sex, diabetes, and level of education. In this model, increasing BMI was significantly associated with lower risk of LTBI (OR = 0.85; 95%CI: 0.77-0.96, P < .01). Conclusion: This study further establishes an inverse relationship between BMI and prevalence of LTBI. Decreased BMI can be considered as a risk factor in LTBI, the reservoir for active TB cases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".