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The use of systematic reviews to collate and summarise evidence is considered one of the great intellectual achievements of recent times. Evidence synthesis has had far-reaching impacts and has helped to inform decision-making in multiple fields. However, it also faces three problems: (i) reviews cannot be relevant to everyone, hampering decision-making, (ii) multiple conflicting review are often produced, and (iii) reviews can become out-of-date rapidly. Here we present a solution to these problems that we term 'dynamic meta-analysis.' With a tool we have developed, users can filter and weight evidence depending on their interests to produce analyses that are highly relevant to their needs.
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Stored only when both papers are in the corpus — 16 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.