The Three-Class Annotation Method Improves the AI Detection of Early-Stage Osteosarcoma on Plain Radiographs: A Novel Approach for Rare Cancer Diagnosis.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
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
16 authors.
Joe HaseiDepartment of Medical Information and Assistive Technology Development, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan.ORCID 0000-0002-4028-0786
Ryuichi NakaharaScience of Functional Recovery and Reconstruction, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan.
Yujiro OtsukaDepartment of Radiology, Juntendo University School of Medicine, Tokyo 113-8431, Japan.
Yusuke NakamuraPlusman LCC, Tokyo 103-0023, Japan.
Kunihiro IkutaDepartment of Orthopedic Surgery, Graduate School of Medicine, Nagoya University, Nagoya 464-0083, Japan.ORCID 0009-0003-1378-9477
Shuhei OsakiDepartment of Musculoskeletal Oncology and Rehabilitation, National Cancer Center Hospital, Tokyo 104-0045, Japan.ORCID 0000-0002-0498-5017
Tamiya HironariDepartment of Musculoskeletal Oncology Service, Osaka International Cancer Institute, Osaka 541-8567, Japan.ORCID 0000-0003-0455-8047
Shinji MiwaDepartment of Orthopedic Surgery, Kanazawa University Graduate School of Medical Sciences, Ishikawa 920-8641, Japan.ORCID 0000-0002-5962-8287
Shusa OhshikaDepartment of Orthopaedic Surgery, Hirosaki University Graduate School of Medicine, Aomori 036-8563, Japan.
Shunji NishimuraDepartment of Orthopaedic Surgery, Kindai University Hospital, Osaka 589-8511, Japan.
Naoaki KaharaDepartment of Orthopedic Surgery, Mizushima Central Hospital, Kurashiki 712-8064, Japan.
Aki YoshidaScience of Functional Recovery and Reconstruction, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan.ORCID 0000-0002-4790-933X
Tomohiro FujiwaraScience of Functional Recovery and Reconstruction, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan.ORCID 0000-0003-4257-9180
Eiji NakataScience of Functional Recovery and Reconstruction, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan.ORCID 0000-0002-5971-4142
Toshiyuki KunisadaScience of Functional Recovery and Reconstruction, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan.ORCID 0000-0002-1775-850X
Toshifumi OzakiScience of Functional Recovery and Reconstruction, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan.ORCID 0000-0003-1732-9307
Funding
Japan Agency for Medical Research and Development (AMED) 22579674Japan Society for the Promotion of Science (JSPS) as part of the Grants-in-Aid for Scientific Re-search (KAKENHI) 21K09228
6 · The paper itself
Abstract
PubMed holds no abstract for this paper.
Indexed as
anatomical annotation methodmedical image annotationosteosarcomarare cancer
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.
The Three-Class Annotation Method Improves the AI Detection of Early-Stage Osteosarcoma on Plain Radiographs: A Novel Approach for Rare Cancer Diagnosis. · full record | OpenQuestion