A new arXiv paper compares label-based searches with a two-stage method that first finds text similar in meaning, then uses a language model to filter results for relevance. In a corpus of 7,354 randomized trials, the researchers say the method found 92% of the evidence a systematic review would include, compared with 45% for searches using curated labels. The paper notes that costs rise with search depth.
Why it matters
For teams preparing systematic reviews, the approach could help surface evidence that does not fit preset labels, though the paper says deeper searches cost more.
