A Bibliometric Analysis of Preprints in Traditional, Complementary, and Integrative Medicine Research
Bibliographic record
Abstract
Abstract Background Traditional, complementary, and integrative medicine (TCIM) encompasses a wide range of healthcare practices and is of growing global interest. Preprints, scientific manuscripts posted prior to formal peer review, offer opportunities to address challenges in TCIM research, including limited funding, publication delays, and concerns about methodological quality. By increasing the visibility and speed of research dissemination, preprints may help strengthen the TCIM evidence base. This bibliometric analysis examined the characteristics of TCIM-related preprints posted on servers with TCIM subject filters. Methods Preprints were sourced from the ASAPbio preprint server directory, limited to servers with TCIM-related categories. Data extraction (June 4, 2024 to January 14, 2025) included title, DOI, abstract, authors, affiliations, preprint posted date, publication journal (if applicable), publication date, preprint type, keywords, number of versions, citations, comments, and funding information. Author geographic distribution, collaboration networks, and key research topics were also analyzed. Results Between 2012 and 2024, 1,980 TCIM preprints were posted across 11 servers. Research Square hosted the most preprints, and China contributed the highest number. Among the 612 preprints later published in journals, BMC Trials was the most common destination, with a median time of 4.89 months from preprint to publication. Funding information was often missing, but when reported, the National Natural Science Foundation of China was the most frequent sponsor. Overall, citation and comment activity was low. Wellcome Open Research had the highest average citations and comments per preprint among all servers. Conclusion This study provides the first in-depth analysis of TCIM preprints, revealing active research areas and important gaps in preprint usage, geographical representation, and post-publication engagement. Findings highlight opportunities to improve transparency and research dissemination in TCIM through more consistent preprint practices and tracking.
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 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 | 0.041 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.323 | 0.367 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".