Article in Diagnostics (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
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.
2 · The registry
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.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
10 authors.
Nazugum AshimovaDepartment of Gastroenterology, Asfendiyarov Kazakh National Medical University, Almaty 050012, Kazakhstan.ORCID 0000-0003-1011-1452
Aigul RaissovaDepartment of Gastroenterology, Asfendiyarov Kazakh National Medical University, Almaty 050012, Kazakhstan.
Elmira KuantayDepartment of Gastroenterology, Asfendiyarov Kazakh National Medical University, Almaty 050012, Kazakhstan.
Moldir KhozhakhmedovaInstitute of Gastroenterology, Hepatology and Metabolism "Interna Clinic", Almaty 050000, Kazakhstan.
Nurgul AldabergenovaInstitute of Gastroenterology, Hepatology and Metabolism "Interna Clinic", Almaty 050000, Kazakhstan.
Madina SuleimenovaAI and Big Data Department, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan.ORCID 0009-0003-8553-5353
Kuat AbzaliyevAI and Big Data Department, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan.
Nassyrova NargizaDepartment of Research, Kazakhstan-Russian Medical University, Almaty 050004, Kazakhstan.ORCID 0000-0001-7458-457X
Ruslan KulmanbetovDepartment of Orthodontics, Asfendiyarov Kazakh National Medical University, Almaty 050012, Kazakhstan.
Alexander NersesovDepartment of Gastroenterology, Asfendiyarov Kazakh National Medical University, Almaty 050012, Kazakhstan.ORCID 0000-0002-8601-3966
Funding
Asfendiyarov Kazakh National Medical University grant No. 0125РКИ0123, Order No. 483, July 18, 2024
6 · The paper itself
Abstract
PubMed holds no abstract for this paper.
Indexed as
autoimmune hepatitiscirrhosismachine learningoverlap syndromepredictive model
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.
Use of Machine Learning in Predicting the Risk of Cirrhosis in Autoimmune Hepatitis Based on Clinical and Immunological Indicators. · full record | OpenQuestion