ReviewDer Orthopade2018
[Big data and deep learning in preventive and rehabilitation medicine].
Review in Der Orthopade, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- AN INTELLIGIBLE AI-DRIVEN DECISION SUPPORT SYSTEM FOR POSTSTROKE MOBILITY ASSESSMENT.Journal of rehabilitation medicine. Clinical communications · 2025Article
- A Novel Multiple-Cue Observational Clinical Scale for Functional Evaluation of Gait After Stroke - The Stroke Mobility Score (SMS).Medical science monitor : international medical journal of experimental and clinical research · 2020Article
- [Handball injuries and their prevention].Der Orthopade · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The digitalization in medicine has led to almost universal availability of information to different healthcare professionals and accelerated clinical pathways. Fast-track concepts and short hospital stays require intelligent and practicable systems in preventive and rehabilitation medicine. This includes optimization of movement analysis by innovative tools such as detectors sensing skin movements, portable feedback systems for monitoring, robot-assisted devices, and prevention programs based on reliable data. Finally, clinical structures are needed to exploit the maximal potential of artificial intelligence (AI) and deep learning. One example is the establishment of inter- and transdisciplinary professional teams such as a RehaBoard. In contrast to other cost-intensive disciplines such as oncology, the introduction of AI into rehabilitation orthopedics and trauma surgery with the support of cross-sectoral cooperation has great potential for performing well in patient benefit-orientated competition (value-based competition).
Indexed as
Identifiers
30039466What 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.