ReviewPatterns (New York, N.Y.)2022
Can we share models if sharing data is not an option?
Review in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
4 citing papers in PubMed.
- The Potential of AI in Nursing Care: Multicenter Evaluation in Fall Risk Assessment.Journal of medical Internet research · 2025Article
- A scoping review of the governance of federated learning in healthcare.NPJ digital medicine · 2025Article
- Unified fair federated learning for digital healthcare.Patterns (New York, N.Y.) · 2024Article
- Cross-border data sharing through the lens of research ethics committee members in sub-Saharan Africa.PloS one · 2024Article
Corrections and comments
- Commented on by
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the big data era, vast volumes of data are generated daily as the foundation of data-driven scientific discovery. Thanks to the recent open data movement, much of these data are being made available to the public, significantly advancing scientific research and accelerating socio-technical development. However, not all data are suitable for opening or sharing because of concerns over privacy, ownership, trust, and incentive. Therefore, data sharing remains a challenge for specific data types and holders, making a bottleneck for further unleashing the potential of these "closed data." To address this challenge, in this perspective, we conceptualize the current practices and technologies in data collaboration in a data-sharing-free manner and propose a concept of the model-sharing strategy for using closed data without sharing them. Supported by emerging advances in artificial intelligence, this strategy will unleash the large potential in closed data. Moreover, we show the advantages of the model-sharing strategy and explain how it will lead to a new paradigm of big data governance and collaboration.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.