TB screening accuracy is a central theme in infectious disease surveillance. A South African study found standard symptom screening detected about 22% of tuberculosis cases in people with diabetes, while chest X-ray detected 56%, suggesting the tools underperform in a high-risk subgroup. In bloodstream infections, researchers reported a four-variable prediction model that could flag bacteremia in febrile cancer patients within hours by combining procalcitonin, pulse rate, neutrophil-to-lymphocyte ratio, and albumin. The clinical and operational implication is that high-risk populations—diabetes and cancer—need tailored diagnostic strategies rather than relying on universal screening thresholds. Together, the studies push toward risk-stratified algorithms and more sensitive diagnostic pipelines in settings where delayed detection worsens outcomes.