This paper is in the following e-collection/theme issue:

Short Paper (25) Artificial Intelligence (1602) Machine Learning (1627) Epublishing and Open Access (94) Scientometrics, Infometrics, and Altmetrics (102)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42789, first published .
Evaluating the Ability of Open-Source Artificial Intelligence to Predict Accepting-Journal Impact Factor and Eigenfactor Score Using Academic Article Abstracts: Cross-sectional Machine Learning Analysis

Evaluating the Ability of Open-Source Artificial Intelligence to Predict Accepting-Journal Impact Factor and Eigenfactor Score Using Academic Article Abstracts: Cross-sectional Machine Learning Analysis

Evaluating the Ability of Open-Source Artificial Intelligence to Predict Accepting-Journal Impact Factor and Eigenfactor Score Using Academic Article Abstracts: Cross-sectional Machine Learning Analysis

Authors of this article:

Carmelo Macri1 Author Orcid Image ;   Stephen Bacchi2 Author Orcid Image ;   Sheng Chieh Teoh2 Author Orcid Image ;   Wan Yin Lim3 Author Orcid Image ;   Lydia Lam2 Author Orcid Image ;   Sandy Patel3 Author Orcid Image ;   Mark Slee4 Author Orcid Image ;   Robert Casson2 Author Orcid Image ;   WengOnn Chan2 Author Orcid Image
Article Authors Cited by (1) Tweetations (18) Metrics

Journals

  1. Crawford E, Gergoudis M, Whitaker S, Laubach L, Yeramosu T, Patel N. Factors for predicting time to acceptance in the journal of the American academy of orthopaedic surgeons. European Journal of Orthopaedic Surgery & Traumatology 2025;35(1) View