Human-Written vs AI-Generated Texts in Orthopedic Academic Literature: Comparative Qualitative Analysis
Bibliographic record
Abstract
BACKGROUND: As large language models (LLMs) are becoming increasingly integrated into different aspects of health care, questions about the implications for medical academic literature have begun to emerge. Key aspects such as authenticity in academic writing are at stake with artificial intelligence (AI) generating highly linguistically accurate and grammatically sound texts. OBJECTIVE: The objective of this study is to compare human-written with AI-generated scientific literature in orthopedics and sports medicine. METHODS: Five original abstracts were selected from the PubMed database. These abstracts were subsequently rewritten with the assistance of 2 LLMs with different degrees of proficiency. Subsequently, researchers with varying degrees of expertise and with different areas of specialization were asked to rank the abstracts according to linguistic and methodological parameters. Finally, researchers had to classify the articles as AI generated or human written. RESULTS: Neither the researchers nor the AI-detection software could successfully identify the AI-generated texts. Furthermore, the criteria previously suggested in the literature did not correlate with whether the researchers deemed a text to be AI generated or whether they judged the article correctly based on these parameters. CONCLUSIONS: The primary finding of this study was that researchers were unable to distinguish between LLM-generated and human-written texts. However, due to the small sample size, it is not possible to generalize the results of this study. As is the case with any tool used in academic research, the potential to cause harm can be mitigated by relying on the transparency and integrity of the researchers. With scientific integrity at stake, further research with a similar study design should be conducted to determine the magnitude of this issue.
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.
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 | 0.094 | 0.286 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".