The Influence of an Artificial Intelligence Large Language Model (ChatGPT) on Orthopaedic Scientific Publishing: A Bibliometric Analysis
Bibliographic record
Abstract
PURPOSE: This study aimed to assess bibliometric trends in orthopaedic research before and after the public release of ChatGPT. METHODS: A bibliometric analysis was conducted using PubMed data from January 2021 to March 2025, encompassing articles from ten high-impact orthopaedic journals. Trends in daily publication frequency, number of co-authors per article, sentence length, and lexical diversity were compared between pre- and post-ChatGPT periods. RESULTS: A total of 19,380 articles were analysed. The mean number of publications per day increased significantly from 9.76 ± 6.79 to 12.02 ± 7.83 (p < 0.001). This difference remained significant after adjusting for monthly variation (p < 0.001). The mean number of authors per article rose from 5.9 ± 3.88 to 6.18 ± 4.04 (p < 0.001). Abstracts became slightly more concise, with the average sentence length decreasing from 14.95 ± 5.13 to 14.67 ± 5.04 (p < 0.001), while lexical diversity increased marginally (TTR: 0.5192 to 0.5233; p < 0.001). CONCLUSION: Since the introduction of ChatGPT, orthopaedic publications have shown a measurable rise in daily output, enhanced collaborative authorship, and subtle changes in linguistic style. These findings suggest a potential influence of AI-assisted tools on the way scientific research is written and disseminated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".