ReviewUrologie (Heidelberg, Germany)2023
[Imaging for urolithiasis].
Review in Urologie (Heidelberg, Germany), 2023. 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, 5 citations in OpenAlex.
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
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The substantial reduction of radiation exposure using (ultra-)low dose programs in native computed tomographic imaging has led to considerable changes in imaging diagnostics and treatment planning in urolithiasis in recent years. In addition, especially in Germany, ultrasound diagnostics is highly available in terms of equipment and with increasing expertise. This can largely replace the previous radiation-associated procedures in emergency and follow-up diagnostics, but also in intraoperative imaging, e.g., in percutaneous stone therapy (intraoperative fluoroscopy). This is reflected in the international guidelines, which recommend these two modalities as first-line diagnostics in all areas mentioned. Continuous technical development enables ever higher resolution imaging and thus improved diagnostics with high sensitivity and specificity. This also enables reliable imaging of particularly vulnerable patient groups, such as children or pregnant women. In addition, methods from the field of artificial intelligence (AI; machine learning, deep learning) are increasingly being used for automated stone detection and stone characterization including its composition. Furthermore, AI models can provide prognosis models as well as individually tailored treatment, follow-up, and prophyaxis. This will enable further personalization of diagnostics and therapy in the field of urolithiasis.
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.