Web-Based Platform for Systematic Reviews and Meta-Analyses of Traditional Chinese Medicine: Platform Development Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».