Comparison of blockchain frameworks for healthcare applications
Bibliographic record
Abstract
Though a relatively new technology, blockchain has become a very trendy topic in recent times, thanks to bitcoin and other popular cryptocurrencies which are built on the blockchain. With features such as decentralized consensus and data immutability, blockchain transactions are known to be transparent, secure, and trustworthy. For these reasons, blockchain is increasingly being adopted in different industries and for diverse use cases, especially where security and trust are important concerns. Health care is one such industry that offers several use cases for applying blockchain technology. Even so, blockchain‐based healthcare applications have yet to become widespread. This is mostly because initial efforts were focused on developing blockchain frameworks for cryptocurrencies and not for general purpose applications, such as health care. Recently, general‐purpose blockchain frameworks, which may be used to develop healthcare applications, have begun to emerge. However, there is no consensus on which framework is most suitable for developing healthcare applications. In light of this, this paper compares the popular general‐purpose blockchain frameworks, vis‐a‐vis the requirements for healthcare systems, in order to guide health informatics researchers and practitioners in selecting the appropriate platform for developing and experimenting with blockchain‐based healthcare applications.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".