Transmission rates were lower than those for seasonal influenza.

Clinicians frequently use influenza rapid antigen tests for diagnostic testing. We tested nasal wash samples from 1 April to 7 June 2009 from 1538 patients using the QuickVue Influenza A+B (Quidel) rapid influenza antigen test and compared the results with real-time reverse transcription polymerase chain reaction (rRT-PCR) assay (gold standard). The prevalence of 2009 pandemic influenza A (pH1N1) was 1.98%, seasonal influenza type A .87%, and seasonal influenza type B 2.07%. The sensitivity and specificity of the rapid test for pH1N1 was 20% (95% CI, 8-39) and 99% (95% CI, 98-99), for seasonal influenza type A 15% (95% CI, 2-45) and 99% (95% CI, 98-99), and for influenza type B was 31% (95% CI, 9-61) and 99% (95% CI, 98-99.7). Rapid influenza antigen tests were of limited use at a time when the prevalence of pH1N1 and seasonal influenza in the United States was low. Clinicians should instead rely on clinical impression and laboratory diagnosis by rRT-PCR.

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Large Language Models (LLMs) have in recent years demonstrated impressive prowess in natural language generation. A common practice to improve generation diversity is to sample multiple outputs from the model. However, partly due to the inaccessibility of LLMs, there lacks a simple and robust way of selecting the best output from these stochastic samples. As a case study framed in the context of question generation, we propose two prompt-based approaches, namely round-trip and prompt-based score, to selecting high-quality questions from a set of LLM-generated candidates. Our method works without the need to modify the underlying model, nor does it rely on human-annotated references -both of which are realistic constraints for real-world deployment of LLMs. With automatic as well as human evaluations, we empirically demonstrate that our approach can effectively select questions of higher qualities than greedy generation.1 * Equal contribution. 1 We open-source all code and annotated data on github.

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