ArticleTransplant international : official journal of the European Society for Organ Transplantation2019
Predicting future citation counts of scientific manuscripts submitted for publication: a cohort study in transplantology.
Article in Transplant international : official journal of the European Society for Organ Transplantation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Evaluation of editors' abilities to predict the citation potential of research manuscripts submitted toBMJ (Clinical research ed.) · 2022Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Citations are widely used for measuring scientific impact. The goal of the present study was to predict citation counts of manuscripts submitted to Transplant International (TI) in the two calendar years following publication. We considered a comprehensive set of 21 manuscript, author, and peer-review-related predictor variables available early in the peer-review process. We also evaluated how successfully the peer-review process at TI identified and accepted the most promising manuscripts for publication. A developed predictive model with nine selected variables showed acceptable test performance to identify often cited articles (AUROC = 0.685). Particularly important predictors were the number of pages, month of publication, publication type (review versus other), and study on humans (yes versus no). Accepted manuscripts at TI were cited more often than rejected but elsewhere published manuscripts (median 4 vs. 2 citations). The predictive model did not outperform the actual editorial decision. Both findings suggest that the peer-review process at TI, in its current form, was successful in selecting submitted manuscripts with a high scientific impact in the future. Predictive models might have the potential to support the review process when decisions are made under great uncertainty.
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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.