ReviewJournal of clinical medicine2026
From Bits to Bedside: From Data to Knowledge in Vascular Surgery.
Review in Journal of clinical medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Data-Information-Knowledge (DIK) hierarchy describes how clinical observations are progressively transformed into actionable knowledge. Although digital technologies are beginning to reshape vascular surgery, their impact is often discussed in isolation rather than as components of a continuous knowledge-generation process. This narrative review applies the DIK framework to examine how recent technological advances are transforming each stage of knowledge generation in vascular surgery. Emphasis was placed on data modeling, computer vision and natural language processing, as well as retrieval-augmented generation (RAG)-based large language model applications. Despite these advances, limitations remain, including data quality, external validation, generalizability, and the reliability and transparency of generated recommendations. Maximizing future knowledge generation requires coordinated advances across all three layers, especially on the data layer.
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.