ReviewArthroscopy, sports medicine, and rehabilitation2025
Artificial Intelligence-Driven Analysis Identifies Anterior Cruciate Ligament Reconstruction, Hip Arthroscopy and Femoroacetabular Impingement Syndrome, and Shoulder Instability as the Most Commonly Published Topics in Arthroscopy.
Review in Arthroscopy, sports medicine, and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled 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.
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, 1 synthesis or guideline pooled it.
- ChatGPT provides accurate and safe responses to patient questions on hip arthroscopy, while completeness remains variable: A systematic review and single-arm meta-analysis.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Pooled it
- Artificial intelligence-driven analysis identifies arthroscopic shoulder surgery, meniscus injury and treatment, and total knee arthroplasty design biomechanics as the most commonly published topics inJournal of experimental orthopaedics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To use advanced topic modeling, specifically the Bidirectional Encoder Representations from Transformers Topic (BERTopic) Model, to analyze research topics in Methods: Text data from the titles and abstracts of 7,304 original articles and reviews published in Results: The BERTopic model categorized 6,901 articles into 35 topics. The most common topics from 1985 to 2023 were anterior cruciate ligament reconstruction, hip arthroscopy and femoroacetabular impingement (FAI), and shoulder instability. From 2020 to 2023, hip arthroscopy and femoroacetabular impingement, superior capsular reconstruction, and anterior cruciate ligament reconstruction were identified as "hot" or popular topics, whereas suture anchor biomechanics, platelet-rich plasma, and arthroscopic irrigation were identified as "cold" topics, indicating a decline in popularity. Conclusions: Using BERTopic, the study showed an efficient way to analyze large amounts of data to establish patterns within orthopaedic sports medicine literature. This study shows the capacity of the BERTopic model to synthesize thousands of articles within Clinical Relevance: Using AI to investigate topics a journal has published will allow us to recognize patterns, identifying common topics, emerging topics, and shifts in focus over time. It will also allow us to identify research gaps that may need to be addressed.
Identifiers
What OpenQuestion holds
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.