Extraocular muscle enlargement and proptosis in carotid cavernous fistulas.

PURPOSE: To evaluate the prevalence and pattern of extraocular muscle enlargement and proptosis in patients with carotid cavernous fistulas (CCF). METHODS: We conducted a retrospective study on patients with digital subtraction angiography (DSA) confirmed CCFs with neuroimaging (computed tomography or magnetic resonance imaging) performed prior to the DSA. The maximum extraocular muscle diameters were recorded. Extraocular muscles were considered enlarged if they were greater than two standard deviations above the normal muscle diameters. Proptosis was defined as the distance between the interzygomatic line to the anterior globe of ≥2 mm compared to the contralateral orbit or ≥21 mm. RESULTS: Forty orbits from 20 patients were included. The mean age of participants was 65 ± 15 years and 13 (65%) were female. Thirteen (65%) fistulas were indirect and seven (35%) were direct. There was enlargement of at least one muscle in 11 (27.5%) orbits, and this was not correlated with the type of fistula (direct/indirect). The inferior rectus was most commonly enlarged in seven orbits (17.5%), followed by the medial rectus in five orbits (12.5%). Proptosis was found in 17 (43%) orbits and was more common ipsilateral to the fistula (58% ipsilateral group vs 19% contralateral group, p < .01). CONCLUSION: Extraocular muscle enlargement was observed in over one-fourth of CCFs. When enlarged, the inferior and medial rectus muscles are most commonly involved. These findings may help clinicians and radiologists when evaluating the CT or MRI scans of patients with suspected CCFs.

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.

BACKGROUND: Strategies to improve the selection of appropriate target journals may reduce delays in disseminating research results. Machine learning is increasingly used in content-based recommender algorithms to guide journal submissions for academic articles. OBJECTIVE: We sought to evaluate the performance of open-source artificial intelligence to predict the impact factor or Eigenfactor score tertile using academic article abstracts. METHODS: PubMed-indexed articles published between 2016 and 2021 were identified with the Medical Subject Headings (MeSH) terms "ophthalmology," "radiology," and "neurology." Journals, titles, abstracts, author lists, and MeSH terms were collected. Journal impact factor and Eigenfactor scores were sourced from the 2020 Clarivate Journal Citation Report. The journals included in the study were allocated percentile ranks based on impact factor and Eigenfactor scores, compared with other journals that released publications in the same year. All abstracts were preprocessed, which included the removal of the abstract structure, and combined with titles, authors, and MeSH terms as a single input. The input data underwent preprocessing with the inbuilt ktrain Bidirectional Encoder Representations from Transformers (BERT) preprocessing library before analysis with BERT. Before use for logistic regression and XGBoost models, the input data underwent punctuation removal, negation detection, stemming, and conversion into a term frequency-inverse document frequency array. Following this preprocessing, data were randomly split into training and testing data sets with a 3:1 train:test ratio. Models were developed to predict whether a given article would be published in a first, second, or third tertile journal (0-33rd centile, 34th-66th centile, or 67th-100th centile), as ranked either by impact factor or Eigenfactor score. BERT, XGBoost, and logistic regression models were developed on the training data set before evaluation on the hold-out test data set. The primary outcome was overall classification accuracy for the best-performing model in the prediction of accepting journal impact factor tertile. RESULTS: There were 10,813 articles from 382 unique journals. The median impact factor and Eigenfactor score were 2.117 (IQR 1.102-2.622) and 0.00247 (IQR 0.00105-0.03), respectively. The BERT model achieved the highest impact factor tertile classification accuracy of 75.0%, followed by an accuracy of 71.6% for XGBoost and 65.4% for logistic regression. Similarly, BERT achieved the highest Eigenfactor score tertile classification accuracy of 73.6%, followed by an accuracy of 71.8% for XGBoost and 65.3% for logistic regression. CONCLUSIONS: Open-source artificial intelligence can predict the impact factor and Eigenfactor score of accepting peer-reviewed journals. Further studies are required to examine the effect on publication success and the time-to-publication of such recommender systems.

Clinical Impact of Pelvic Malrotation on Radiograph-Based Preoperative Planning for Total Hip Arthroplasty: A Proof-of-Concept and Prudent Prediction of Acceptable Rotation.

Introduction: Pelvic rotation (PR) on preoperative radiograph templating can affect various critical measured acetabular angles and potentially outcomes of successful total hip arthroplasty (THA). Optimising anatomical reconstruction of the joint is essential to achieve function, longevity and prevention of complications following surgery. There is limited literature that standardises the degree of acceptable PR on radiograph or its effects on the fitting of acetabular prostheses. Objective: This study aimed to develop a proof-of-concept that quantifies how PR can affect various acetabular angles used in pre-operative THA templating and to formulate a practicable method of determining if the preoperative PR is acceptable. Materials and methods: Computerised tomography (CT) models from three control and two THA patients were generated and manipulated in various degrees of PR. CT slices were thickened to simulate radiographs and acetabular angles measured. Results: The acetabular anteversion distance (AAD) and lateral opening angle (LOA) demonstrated a linear and quadratic relationship with good correlation (R 2 = 0.923, R 2 = 0.710 respectively, p < 0.0001) in relation to PR. Change in area of prosthesis (AOP) demonstrated a good linear correlation (r 2 = 0.774 and r 2 = 0.875, p < 0.0001) with PR. Two novel measurements were used to estimate the degree of PR from a pelvic radiograph; the horizontal distance between pubic symphysis and middle of sacrococcygeal joint (PSSC) and the simplified pelvic rotation ratio (SPRR). A strong correlation between PSSC and SPRR with change in PR was observed (R 2 = 0.970, R 2 = 0.953, p < 0.001). Conclusion: Preliminary result suggests that an SPRR > 2.0 correlates to PR > 20° with potential to have a clinical impact on preoperative measurements.