PreprintMatch: A tool for preprint to publication detection shows global inequities in scientific publication

Eckmann, Peter; Bandrowski, Anita

doi:10.1371/journal.pone.0281659

Cited by 35 publications

(12 citation statements)

References 61 publications

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“…While over half (58%) of preprints are subsequently published in a peer-reviewed journal [ 40 ], the fact is that some will remain unpublished, due to journal rejection because of poor methodological and statistical quality or, in rare cases, lack of submission. Based on this, some suggest that preprints should be excluded from meta-analyses [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

Comparison of effect estimates between preprints and peer-reviewed journal articles of COVID-19 trials

Davidson,

Evrenoglou,

Graña

et al. 2024

BMC Med Res Methodol

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Background Preprints are increasingly used to disseminate research results, providing multiple sources of information for the same study. We assessed the consistency in effect estimates between preprint and subsequent journal article of COVID-19 randomized controlled trials. Methods The study utilized data from the COVID-NMA living systematic review of pharmacological treatments for COVID-19 (covid-nma.com) up to July 20, 2022. We identified randomized controlled trials (RCTs) evaluating pharmacological treatments vs. standard of care/placebo for patients with COVID-19 that were originally posted as preprints and subsequently published as journal articles. Trials that did not report the same analysis in both documents were excluded. Data were extracted independently by pairs of researchers with consensus to resolve disagreements. Effect estimates extracted from the first preprint were compared to effect estimates from the journal article. Results The search identified 135 RCTs originally posted as a preprint and subsequently published as a journal article. We excluded 26 RCTs that did not meet the eligibility criteria, of which 13 RCTs reported an interim analysis in the preprint and a final analysis in the journal article. Overall, 109 preprint–article RCTs were included in the analysis. The median (interquartile range) delay between preprint and journal article was 121 (73–187) days, the median sample size was 150 (71–464) participants, 76% of RCTs had been prospectively registered, 60% received industry or mixed funding, 72% were multicentric trials. The overall risk of bias was rated as ‘some concern’ for 80% of RCTs. We found that 81 preprint–article pairs of RCTs were consistent for all outcomes reported. There were nine RCTs with at least one outcome with a discrepancy in the number of participants with outcome events or the number of participants analyzed, which yielded a minor change in the estimate of the effect. Furthermore, six RCTs had at least one outcome missing in the journal article and 14 RCTs had at least one outcome added in the journal article compared to the preprint. There was a change in the direction of effect in one RCT. No changes in statistical significance or conclusions were found. Conclusions Effect estimates were generally consistent between COVID-19 preprints and subsequent journal articles. The main results and interpretation did not change in any trial. Nevertheless, some outcomes were added and deleted in some journal articles.

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Section: Discussionmentioning

confidence: 99%

Comparison of effect estimates between preprints and peer-reviewed journal articles of COVID-19 trials

Davidson,

Evrenoglou,

Graña

et al. 2024

BMC Med Res Methodol

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“…Moreover, reviewers and editors may be more willing to perform preprint review for authors within their existing networks, potentially reinforcing Matthew effects (i.e., benefits accrue to those who are already privileged) [ 39 ]. On the other hand, preprints lower barriers to sharing: many preprints are never published in a journal, and this fraction varies from approximately 20% of preprints from researchers in high-income countries to approximately 40% of preprints from researchers in low-income countries, and is correlated with funding disclosures [ 26 ]. This suggests that preprints enable the release of scientific outputs that would not otherwise be shared.…”

Section: Methodsmentioning

confidence: 99%

Recommendations for accelerating open preprint peer review to improve the culture of science

Avissar-Whiting,

Belliard,

Bertozzi

et al. 2024

PLoS Biol

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Peer review is an important part of the scientific process, but traditional peer review at journals is coming under increased scrutiny for its inefficiency and lack of transparency. As preprints become more widely used and accepted, they raise the possibility of rethinking the peer-review process. Preprints are enabling new forms of peer review that have the potential to be more thorough, inclusive, and collegial than traditional journal peer review, and to thus fundamentally shift the culture of peer review toward constructive collaboration. In this Consensus View, we make a call to action to stakeholders in the community to accelerate the growing momentum of preprint sharing and provide recommendations to empower researchers to provide open and constructive peer review for preprints.

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“…Publication searches were limited to articles indexed by PubMed, which covers more than 80% of the peer-reviewed biomedical literature [58]. Preprint servers such as bioRxiv allow researchers to share study results before peer review, however do not guarantee publication in a peerreviewed journal [59,60]. Whilst peer-reviewed publications should not be interpreted in isolation as a marker for study quality, preprints were excluded from this study as they are generally not peer-reviewed.…”

Section: Study Limitationsmentioning

confidence: 99%

Planned but ever published? A retrospective analysis of clinical prediction modelling studies registered on clinicaltrials.gov since 2000

White,

Parsons,

Borg

et al. 2023

Preprint

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Objectives: To describe the characteristics and publication outcomes of clinical prediction model studies registered on clinicaltrials.gov since 2000.Design: Retrospective descriptive analysis.Data sources: Information from clinicaltrials.gov records at the time of download and corresponding web histories, PubMed abstracts and metadata, based on searches by National Clinical Trials (NCT) number.Selection criteria: Observational studies registered on clinicaltrials.gov between 1 January 2000 and 2 March 2022 describing the development of a new clinical prediction model or the validation of an existing model for predicting the individual-level prognostic or diagnostic risk.Data extraction: Eligible clinicaltrials.gov records were classified by modelling study type (development, validation) and the model outcome being predicted (prognostic, diagnostic). Recorded characteristics included study status, sample size information, Medical Subject Headings (MeSH), and plans to share Individual Participant Data. Publications describing results from clinical prediction modelling were searched for each record after their reported study start date (last updated 17 November 2023).Results: 928 records were analysed from a possible 89,896 observational study records. Publications searches found 170 matching peer-reviewed publications for 137 clinicaltrials.gov records. The estimated proportion of records with one or more matching publications after accounting for time since study start was 2.8% at 2 years (95% confidence interval; CI: 1.7% to 3.9%), 12.3% at five years (9.8% to 14.9%) and 27.8% at ten years (22.6% to 33.0%). Stratifying records by study start year indicated that publication proportions improved over time. Records tended to prioritise the development of new prediction models over the validation of existing models (75.9%; 704/928 vs 24.1%; 182/928). At the time of download, 27.2% of records were marked as complete, 35.2% were still recruiting, and 14.7% had unknown status. Only 7.4% of records stated plans to share individual participant data.Conclusions: Published clinical prediction model studies represent a fraction of current and ongoing research efforts, with many studies planned but not completed or published. Improving the uptake of study preregistration and follow-up will increase the visibility of planned research. Introducing additional registry features and guidance may improve the identification of clinical prediction model studies posted to clinical registries.

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PreprintMatch: A tool for preprint to publication detection shows global inequities in scientific publication

Cited by 35 publications

References 61 publications

Comparison of effect estimates between preprints and peer-reviewed journal articles of COVID-19 trials

Comparison of effect estimates between preprints and peer-reviewed journal articles of COVID-19 trials

Recommendations for accelerating open preprint peer review to improve the culture of science

Planned but ever published? A retrospective analysis of clinical prediction modelling studies registered on clinicaltrials.gov since 2000

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