JMIRx Med

PubMed-indexed overlay journal for preprints with post-review manuscript marketplace (What is JMIRx?).

Editor-in-Chief:

Edward Meinert, MA (Oxon), MSc, MBA, MPA, MPH, PhD, CEng, FBCS, EUR ING, Professor of Digital Health and Clinical Artificial Intelligence (Translational and Clinical Research Institute), University of Plymouth, United Kingdom


JMIRx Med (ISSN 2563-6316), which has been accepted for indexing in PubMed and PubMed Central, is an innovative overlay journal to  MedRxiv and JMIR Preprints (other preprint servers are invited to join). JMIRx peer-reviews preprints and publishes their revised "version of record" with peer-review reports across a broad range of medical, clinical and related health sciences. Unlike the majority of JMIR journals, papers published in this journal do not require a digital health focus - in fact, most papers we published in the first months of the journal were related to COVID19, but we publish all research that qualifies for preprinting on MedRxiv

Conceived to address the urgent need to make highly relevant scientific information available as early as possible without losing the quality of the peer-reviewed process, this innovative new journal is the first in a new series of “superjournals”. Superjournals (a type of "overlay" journal) sit on top of preprint servers (JMIRx-Med serves MedRxiv and JMIR Preprints), offering peer-review and everything else a traditional scholarly journal does. Our goal is to rapidly peer review and publish a paper. All JMIRx Med papers must have originated as a preprint. 

All JMIRx Med papers are rigorously peer-reviewed, copyedited and XML-tagged. Accepted papers are published along with the related Peer Review Reports and Author Responses to Peer Review Reports, providing an additional layer of transparency to the scholarly publishing process. 

There is no Article Processing Fee directly paid by authors for this journal. JMIRx Med is envisioned as a diamond open access and Plan-P compliant journal, which enables Plan P member universities/institutions and funders to subsidize peer review of preprints and publishing in JMIRx Med. Individual PI-led labs, departments and universities can become institutional members, guaranteeing unlimited peer-review of preprints.

If you are not affiliated with a Plan P member organization, we encourage you to provide Plan P membership details to your administrator or sign up for a Principal Investigator (PI) level membership. Further details provided here.

For a limited time only, authors who opt-in during submission to receive PREreview or PeerRef community peer review for their preprint or refer us to their department head/librarian/funder contact will receive a membership-waiver and may publish the preprint in JMIRx Med at no cost to the author. Referral form provided here.

To submit a preprint to JMIRx, authors can self-nominate their existing preprints for publication (which is the equivalent to a traditional journal submission), using the minimalistic JMIRx-Med submission form that essentially only points to the preprint (the preprint needs to be unpublished and should not be under consideration by a journal).

 

Preprints that have already been peer-reviewed by third-party Plan P accredited peer-review services such as PREreview and PeerRef do not require further peer-review (at the editors' discretion). In the submission process, you can nominate your preprint for a PREreview journal club, which can be used in lieu of traditional peer-review.

 

For more details on other submission pathways (including for papers not in MedRxiv) and peer-review options see How to submit to a JMIRx journal.

For more information on JMIRx please also see our Knowledge Base article "What is JMIRx?".  

 

 

Recent Articles

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Original Papers

Background:The information about the Hounsfield range values for healthy teeth tissues could become an additional tool in assessing dental health and could be used, among other data, for subsequent machine learning.

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#xCardiovascularMedicine

The Society of Thoracic Surgeons (STS), and EuroSCORE II (ES II) risk scores, are the most commonly used risk prediction models for adult cardiac surgery post-operative in-hospital mortality. However, they are prone to miscalibration over time, and poor generalisation across datasets and their use remain controversial. Despite increased interest, a gap in understanding the effect of dataset drift on the performance of ML over time remains a barrier to its wider use in clinical practice. Dataset drift occurs when a machine learning system underperforms because of a mismatch between the dataset it was developed and the data on which it is deployed.

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Preprints Open for Peer-Review

There are no preprints available for open peer-review at this time. Please check back later.

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