Research at the intersection of Traditional, Complementary, and Integrative Medicine and Artificial Intelligence: A protocol for a bibliometric analysis
Bibliographic record
Abstract
ABSTRACT: Background: Traditional, Complementary, and Integrative Medicine (TCIM) describes a broad collection of medical interventions, practices, and belief systems that fall outside the purview of conventional medicine. Accompanying the recent growth of TCIM research productivity, Artificial Intelligence (AI) technologies have increasingly impacted areas of biomedical research, including diagnosis, treatment planning, and drug discovery. Despite the applicability of AI to TCIM research, the intersection of these topics remains underexplored. Methods: A search string encompassing terms related to TCIM and AI will be run on MEDLINE, with no restrictions by date, language, or publication type. Retrieved MEDLINE records will subsequently be used as the source for Digital Object Identifier citation searches in Scopus. No manual cleaning of MEDLINE or Scopus records will be performed, given the comprehensive search strategy and the complexities of defining the TCIM scope. The following bibliometric data will be collected: number of publications (including total publications and publications per year), open access status, subject area, document type, publication stage, publications per journal, author affiliations, funding sponsors, publication country, source type, and publication language. Performance analysis indicators (e.g., citations and publications) will be identified and presented. Bibliometric network maps will be visualized using VOSviewer, with thematic clusters identified and presented. Conclusions: This bibliometric analysis study will provide insights into research productivity at the intersection of TCIM and AI, informing future research directions and policy development.
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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 | 0.175 | 0.279 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.053 | 0.066 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.012 |
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".