Prevalence, Progression, and Treatment of Asymptomatic Tuberculosis: A Prospective Cohort Study in Lanxi County, Zhejiang Province, China
Bibliographic record
Abstract
Abstract Background Individuals with asymptomatic tuberculosis (TB) are considered a significant risk to the disease burden and transmission. However, the progression and treatment for asymptomatic TB remain incompletely described. Methods This prospective cohort study was embedded within a prevalence survey conducted in 2021 and 2022 in Lanxi County, China. All patients with pulmonary TB who consented to participate would be included in the study and were categorized as asymptomatic or symptomatic. For the primary analysis, asymptomatic TB was defined as the absence of current cough, fever, night sweats, weight loss, or hemoptysis. Patients were followed up until 10 November 2024. Results Among 109 345 individuals screened, 193 were included, of whom 101 (52.3%) were symptomatic and 92 (47.7%) were asymptomatic. The proportion of asymptomatic TB varied from 32.5% to 62.7% depending on varying symptom negative threshold. Fewer asymptomatic patients were bacteriologically confirmed compared to symptomatic patients (71.7% [66/92] vs 90.1% [91/101], P = .001). The median time for asymptomatic patients at screening to develop symptoms was 102 days. Most patients in both groups received treatment for active TB (97.8% vs 99.0%, P = .606). The treatment success rate among asymptomatic patients was comparable to that of symptomatic patients (93.3% vs 96.0%, P = .521), but their treatment duration was significantly shorter (196 vs 273 days, P < .001). Conclusions In the community setting, a significant number of TB cases were asymptomatic and remained so for months. These cases demonstrated satisfactory treatment coverage and outcomes, with shorter durations compared to symptomatic TB, suggesting the potential for developing shorter regimens for asymptomatic TB.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".