ArticleHealth science reports2022
Duplication and nonregistration of COVID-19 systematic reviews: Bibliometric review.
Article in Health science reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Mosques as Community Resilience Centers During Disasters: A Systematic Review of COVID-19 Interventions.Journal of community health · 2026Pooled it
- The role of religious leaders in the acceptance of COVID-19 vaccinations: a systematic review.BMC public health · 2025Pooled it
- Exploring duplication in reviews of Long COVID: 2020-2023.Systematic reviews · 2026Article
- Methodological quality and reporting quality of COVID-19 living systematic review: a cross-sectional study.BMC medical research methodology · 2023Article
- Extent and nature of duplication in PROSPERO using COVID-19-related registrations: a retrospective investigation and survey.BMJ open · 2022Article
- Duplication and nonregistration of COVID-19 systematic reviews: Bibliometric review.Health science reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study examines the conduct of systematic reviews during the early stages of the COVID-19 pandemic, including compliance to protocol registration and duplication of reviews on similar topics. The methodological and reporting quality were also explored. Methods: A cross-sectional, bibliometric study was undertaken of all systematic review manuscripts on a COVID-19 intervention published between January 1st and June 30th, 2020. Protocol registration on a publicly accessible database was recorded. Duplication was determined by systematically recording the number of reviews published on each topic of analysis. Methodological quality and reporting quality were assessed using the AMSTAR-2 and PRISMA 2009 instruments, respectively. Results: Thirty-one eligible systematic reviews were identified during the inclusion period. The protocol of only four (12.9%) studies was registered on a publicly accessible database. Duplication was frequent, with 15 (48.4%) of the 31 included studies focusing on either hydroxychloroquine (and/or chloroquine) or corticosteroids. Only one study (3.2%) was of "high" methodological quality, four (12.9%) were "low" quality, and the remainder ( Conclusion: Systematic reviews during the early stages of the COVID-19 pandemic were uncommonly registered, frequently duplicated, and mostly of low methodological quality. In contrast, the reporting quality of manuscripts was generally good but varied substantially across published reports. There is a need for heightened stewardship of systematic review research, particularly during times of medical crisis where the generation of primary evidence may be rapid and unstable.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.