ArticleMedeniyet medical journal2026
Methodological Reporting Quality of Artificial Intelligence-Based Orthopedic Studies: A Literature Analysis.
Article in Medeniyet medical journal, 2026. 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.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: We set out to examine how completely artificial intelligence (AI)-based orthopedic studies report their methods. Methods: A PubMed search covering 2023-2025 was run with a predefined strategy. Screening of titles, abstracts and full texts left 280 eligible papers, and 200 of these were drawn at random for scoring. Each paper was rated against a six-item framework built from the TRIPOD-AI, STARD-AI and CONSORT-AI guidelines. Results: Mean score across the 200 studies was 4.8±0.9. Twenty-eight percent reached 6/6, 37% scored 5/6 and 23% scored 4/6; the remaining 12% scored 3 or below. Model description, dataset characteristics and performance metrics appeared in every paper (100%), and training and validation procedures in 88%. External validation was far less common, reported by only 32%, while 61% included some form of clinical comparison. Conclusions: Reporting in this literature is moderate to high overall, yet two weaknesses persist: external validation and clinical integration. Work in this area would benefit from closer attention to generalizability, clinical relevance and the existing reporting guidelines.
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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.