Evidence map›Paper›PMID 37053223›Full record

ArticlePloS one2023

How many submissions are needed to discover friendly suggested reviewers?

Pedro Pessoa, Steve Pressé

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Simulation-based inference captures non-Markovian effects as exemplified in protein production kinetics through cell division.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Pedro PessoaCenter for Biological Physics, Arizona State University, Tempe, AZ, United States of America.ORCID 0000-0002-6258-5679
Steve PresséCenter for Biological Physics, Arizona State University, Tempe, AZ, United States of America.ORCID 0000-0002-5408-0718

Funding

A Bayesian nonparametric approach to superresolved tracking of multiple molecules inside living cellsR01GM130745 · NIGMS · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI PRESSE, STEVE · 2019 to 2023
$1.5M
Theoretical Models of Single Molecule Dynamics from Minimal Photon NumbersR01GM134426 · NIGMS · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI PRESSE, STEVE · 2019 to 2022
$1.2M
NIGMS NIH HHS R01 GM130745NIGMS NIH HHS R01 GM134426
6 · The paper itself

Abstract

It is common in scientific publishing to request from authors reviewer suggestions for their own manuscripts. The question then arises: How many submissions are needed to discover friendly suggested reviewers? To answer this question, as the data we would need is anonymized, we present an agent-based simulation of (single-blinded) peer review to generate synthetic data. We then use a Bayesian framework to classify suggested reviewers. To set a lower bound on the number of submissions possible, we create an optimistically simple model that should allow us to more readily deduce the degree of friendliness of the reviewer. Despite this model's optimistic conditions, we find that one would need hundreds of submissions to classify even a small reviewer subset. Thus, it is virtually unfeasible under realistic conditions. This ensures that the peer review system is sufficiently robust to allow authors to suggest their own reviewers.

Indexed as

Peer ReviewPublishingBayes TheoremComputer SimulationPeer Review, Research

Identifiers

PMID37053223
PMCPMC10101443

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.