ArticleFrontiers in digital health2023
Building the foundation for a modern patient-partnered infrastructure to study temporomandibular disorders.
Article in Frontiers in digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Application of the Delphi method to the development of common data elements for social drivers of health: A systematic scoping review.Translational behavioral medicine · 2024Article
- Perspective: Advancing the science regarding temporomandibular disorders.Frontiers in dental medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Conflicting reports from varying stakeholders related to prognosis and outcomes following placement of temporomandibular joint (TMJ) implants gave rise to the development of the TMJ Patient-Led RoundTable initiative. Following an assessment of the current availability of data, the RoundTable concluded that a strategically Coordinated Registry Network (CRN) is needed to collect and generate accessible data on temporomandibular disorder (TMD) and its care. The aim of this study was therefore to advance the clinical understanding, usage, and adoption of a core minimum dataset for TMD patients as the first foundational step toward building the CRN. Methods: Candidate data elements were extracted from existing data sources and included in a Delphi survey administered to 92 participants. Data elements receiving less than 75% consensus were dropped. A purposive multi-stakeholder sub-group triangulated the items across patient and clinician-based experience to remove redundancies or duplicate items and reduce the response burden for both patients and clinicians. To reliably collect the identified data elements, the identified core minimum data elements were defined in the context of technical implementation within High-performance Integrated Virtual Environment (HIVE) web-application framework. HIVE was integrated with CHIOS™, an innovative permissioned blockchain platform, to strengthen the provenance of data captured in the registry and drive metadata to record all registry transaction and create a robust consent network. Results: A total of 59 multi-stakeholder participants responded to the Delphi survey. The completion of the Delphi surveys followed by the application of the required group consensus threshold resulted in the selection of 397 data elements (254 for patient-generated data elements and 143 for clinician generated data elements). The infrastructure development and integration of HIVE and CHIOS™ was completed showing the maintenance of all data transaction information in blockchain, flexible recording of patient consent, data cataloging, and consent validation through smart contracts. Conclusion: The identified data elements and development of the technological platform establishes a data infrastructure that facilitates the standardization and harmonization of data as well as perform high performance analytics needed to fully leverage the captured patient-generated data, clinical evidence, and other healthcare ecosystem data within the TMJ/TMD-CRN.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.