Evidence map›Paper›PMID 42654415›Full record

ArticleMedicina (Kaunas, Lithuania)2026

Per-Vertebra Prediction of Future Osteoporotic Fractures from Routine Computed Tomography Using a Two-Stage Machine Learning Framework.

Kirill Riazanovskiy, Dāvids Orlovs, Jekaterina Stepanova, Victor Sineglazov, Ardis Platkajis, Olena Chumachenko

Abstract read
In one paragraph

Article in Medicina (Kaunas, Lithuania), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

6 authors.

Kirill RiazanovskiyDepartment of Artificial Intelligence, Institute for Applied System Analysis, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" (KPI), 03056 Kyiv, Ukraine.ORCID 0000-0002-8771-8060
Dāvids OrlovsDepartment of Radiology, Faculty of Medicine, Rīgas Stradiņa Universitāte (RSU), LV-1007 Rīga, Latvia.ORCID 0009-0008-7428-8592
Jekaterina StepanovaDepartment of Radiology, Faculty of Medicine, Rīgas Stradiņa Universitāte (RSU), LV-1007 Rīga, Latvia.ORCID 0009-0007-8637-8934
Victor SineglazovDepartment of Artificial Intelligence, Institute for Applied System Analysis, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" (KPI), 03056 Kyiv, Ukraine.ORCID 0000-0002-3297-9060
Ardis PlatkajisDepartment of Radiology, Faculty of Medicine, Rīgas Stradiņa Universitāte (RSU), LV-1007 Rīga, Latvia.ORCID 0000-0001-8865-5949
Olena ChumachenkoDepartment of Artificial Intelligence, Institute for Applied System Analysis, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" (KPI), 03056 Kyiv, Ukraine.ORCID 0000-0003-3006-7460

Funding

Latvian Council of Science No. LV-UA/2025/4Ministry of Education and Science 0125U003179
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Lumbar VertebraeMachine LearningOsteoporotic FracturesSpinal FracturesTomography, X-Ray ComputedAbsorptiometry, PhotonAgedAged, 80 and overCohort StudiesFemaleHumansMiddle AgedPredictive Learning ModelsRetrospective Studiescomputed tomographyconditional logistic regressionexpected calibration errorfracture-risk predictionmachine learningopportunistic CTosteoporosisquantitative imaging biomarkersradiomicsvertebral fracture

Identifiers

PMID42654415
PMCPMC13514964

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.