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BACKGROUND: Abstracts of scientific articles should be accurate and detailed in summarizing the information in the full-text because they are the first article section the reader examines. This study assessed the reporting quality of randomized controlled trials (RCTs) abstracts related to implant dentistry and examined associations between reporting quality and study characteristics. METHODS: of January 2021, we searched the PubMed database for abstracts of RCTs published in high-ranked periodontology and implant dentistry journals from 2016 to 2021. For each abstract, we assessed if the Consolidated Standards of Reporting Trials for abstracts (CONSORT-A) checklist items were reported completely, partially, or not reported. An Overall CONSORT Score (OCS) and relative score (OCS%) were calculated as a proxy to checklist adherance. Linear regression models were fitted to analyze associations between trial characteristics and completeness of reporting. RESULTS: Four-hundred and thirty four of the 678 retrieved abstracts were eligible for inclusion. The mean OCS and OCS% were 6,23 (standard deviation [SD] = 1.56) or 41.5% (SD = 10.4), respectively. Items most frequently reported included the title (n = 434; 100%), intended intervention (n = 425; 98%) and conclusions (n = 430; 99%). Participant allocation, blinding, and trial registration were rarely completely reported with frequencies of 2%, 3% and 4%, respectively. We found that number of authors, continent, type of RCT, number of centers, report of ethical approval, funding, structure and length of the abstract were associated with better abstract reporting. CONCLUSION: The reporting quality of abstracts in RCTs related to implant dentistry is suboptimal. Journals should start to incorporate and endorse the use of the CONSORT-A guidelines in their instructions to authors to enhance reporting quality. This article is protected by copyright. All rights reserved.
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Stored only when both papers are in the corpus — 8 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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