The Influence of an Artificial Intelligence Large Language Model (ChatGPT) on Orthopaedic Scientific Publishing: A Bibliometric Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.066 | 0.263 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it