A methodological framework for conducting diagnostic meta-analyses using large literature corpora.

BACKGROUND

The exponential growth of biomedical literature challenges the feasibility, reproducibility, and bias control of diagnostic meta-analyses based on manual screening.

METHODS

We propose a scalable framework integrating automated topic modeling (Latent Dirichlet Allocation, LDA) for thematic pre-screening with hierarchical multivariate meta-analysis to jointly synthesize sensitivity and specificity. Abstracts from eight databases were processed using linguistic normalization, lemmatization, and probabilistic topic modeling to prioritize diagnostically relevant studies. Selected studies were synthesized using bivariate hierarchical random-effects models on the logit scale, allowing incorporation of methodological and clinical moderators.

RESULTS

Applied to dengue diagnostic algorithms in Latin America, the framework reduced 5, 766 retrieved records to 10 studies contributing 94 algorithms. Machine-learning-based models showed significantly higher joint diagnostic performance than traditional statistical models (logit coefficient = 1.5657, p < 0.001), while external validation was associated with a significant loss of performance (-0.7962, p < 0.001). Estimated sensitivity ranged from 37.97% to 86.66% and specificity from 63.26% to 94.81% across algorithm types and phases.

CONCLUSIONS

The proposed workflow offers a methodologically rigorous and scalable approach to diagnostic evidence synthesis that is adaptable to clinical domains characterized by rapid literature growth and heterogeneous diagnostic evidence.