Web-Based Platform for Systematic Reviews and Meta-Analyses of Traditional Chinese Medicine: Platform Development Study
Bibliographic record
Abstract
Background: There are many problems associated with systematic reviews of traditional Chinese medicine (TCM), such as considering "integrated traditional Chinese and Western medicine" or treatment methods as intervention measures without considering the differences in drug use, disregarding dosage and courses of treatment, disregarding interindividual differences in control groups, etc. Classifying a large number of heterogeneous intervention measures into the same measure is easy but results in inaccurate results. In April 2023, Cochrane launched RevMan Web to digitalize systematic reviews and meta-analyses. We believe that this web-based working model helps solve the abovementioned problems. Objective: This study aims to (1) develop a web-based platform that is more suitable for systematic review and meta-analysis of TCM and (2) explore the characteristics and future development directions of this work through the testing of digital workflow. Methods: We developed TCMeta (Traditional Chinese Medicine Meta-analysis)-a platform focused on systematic reviews of TCM types. All systematic review-related work can be completed on the web, including creating topics, writing protocols, arranging personnel, obtaining literature, screening literature, inputting and analyzing data, and designing illustrations. The platform was developed using the latest internet technology and can be continuously modified and updated based on user feedback. When screening the literature on the platform, in addition to the traditional manual screening mode, the platform also creatively provides a query mode where users input keywords and click on Search to find literature with the same characteristics; this better reflects the objectivity of the screening with higher efficiency. Productivity can be improved by analyzing data and generating graphs digitally. Results: We used some test data in TCMeta to simulate data processing in a systematic review. In the literature screening stage, researchers could rapidly screen 19 sources of literature from among multiple sources with the manual screening mode. This traditional method could result in bias due to differences in the researchers' cognitive levels. The query mode is much more complex and involves inputting of data regarding drug compatibility, dosage, syndrome type, etc; different query methods can yield very different results, thus increasing the stringency of screening. We integrated data analysis tools on the platform and used third-party software to generate graphs. Conclusions: TCMeta has shown great potential in improving the quality of systematic reviews of TCM types in simulation tests. Several indicators show that this web-based mode of working is superior to the traditional way. Future research is required to focus on validating and refining the performance of TCMeta, emphasizing the ability to handle complex data. The system has good scalability and adaptability, and it has the potential to have a positive impact on the field of evidence-based medicine.
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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 | 0.122 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".