ArticleLearning health systems2023
Transforming health and well-being through publishing computable biomedical knowledge (CBK).
Article in Learning health systems, 2023. 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.
- Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems.Learning health systems · 2026Article
- Trends in electronic health record metadata use for management purposes.Learning health systems · 2025Article
- Ten simple rules to make computable knowledge shareable and reusable.PLoS computational biology · 2024Article
- Transforming health and well-being through publishing computable biomedical knowledge (CBK).Learning health systems · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computable biomedical knowledge artifacts (CBKs) are software programs that transform input data into practical output. CBKs are expected to play a critical role in the future of learning health systems. While there has been rapid growth in the development of CBKs, broad adoption is hampered by limited verification, documentation, and dissemination channels. To address these issues, the Learning Health Systems journal created a track dedicated to publishing CBKs through a peer-review process. Peer review of CBKs should improve reproducibility, reuse, trust, and recognition in biomedical fields, contributing to learning health systems. This special issue introduces the CBK track with four manuscripts reporting a functioning CBK, and another four manuscripts tackling methodological, policy, deployment, and platform issues related to fostering a healthy ecosystem for CBKs. It is our hope that the potential of CBKs exemplified and highlighted by these quality publications will encourage scientists within learning health systems and related biomedical fields to engage with this new form of scientific discourse.
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.