When to replicate systematic reviews of interventions: consensus checklist is a research paper published in BMJ (2020). On theSindex it has a DataRank of 4.0. It has been cited 109 times, with 81 citing works in its 1-hop citation network.
For systematic reviews of interventions, replication is defined as the \nreproduction of findings of previous systematic reviews looking at the same \neffectiveness question either by: purposefully repeating the same methods to \nverify one or more empirical findings; or purposefully extending or narrowing \nthe systematic review to a broader or more focused question (eg, across broader \nor more focused populations, intervention types, settings, outcomes, or study \ndesigns) \nAlthough systematic reviews are often used as the basis for informing policy \nand practice decisions, little evidence has been published so far on whether \nreplication of systematic reviews is worthwhile \nReplication of existing systematic reviews cannot be done for all topics; any \nunnecessary or poorly conducted replication contributes to research waste \nThe decision to replicate a systematic review should be based on the priority of \nthe research question; the likelihood that a replication will resolve uncertainties, \ncontroversies, or the need for additional evidence; the magnitude of the benefit \nor harm of implementing findings of a replication; and the opportunity cost of \nthe replication \nSystematic review authors, commissioners, funders, and other users (including \nclinicians, patients, and representatives from policy making organisations) can \nuse the guidance and checklist proposed here to assess the need for a replication
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.705
From this paper's citation signal
Citation Network Contribution
3.3
From 68 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 18% comes from its base citations and 82% from the citation network (68 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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