SynthesisThe British journal of radiology2023
Interobserver variability studies in diagnostic imaging: a methodological systematic review.
Synthesis in The British journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Meta-Analysis of Interobserver Agreement in Assessment of Interstitial Lung Disease Using High-Resolution CT.Radiology · 2024Pooled it
- Interobserver variability studies in diagnostic imaging: a methodological systematic review.The British journal of radiology · 2023Pooled it
- Reliability and agreement of CBCT-based alveolar bone assessments for follow-up studies on adolescent orthodontic patients using multiplanar reconstruction and various CBCT units.Progress in orthodontics · 2026Trial
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- Computed Tomographic Lesion Descriptors in Foals With Hematogenous Osteomyelitis: Interobserver Agreement and Association With Outcome.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2026Article
- Observable Classification Patterns and Diagnostic Uncertainty in Parotid Ultrasound: A Multimethod Secondary Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Posterior Communicating Artery Aneurysm Microsurgery: PComA-CORE, an Anatomy-Informed Explainable AI Framework for Complexity, Neurovascular Risk, Oculomotor Recovery and Functional Outcome.Medical sciences (Basel, Switzerland) · 2026Article
- Machine Learning-Based Cytokine Endotyping of Thyroid Eye Disease.Investigative ophthalmology & visual science · 2026Article
- Deep learning reconstruction-applied PROPELLER diffusion-weighted imaging in uterine malignancies: a comparison study with MUSE.Japanese journal of radiology · 2026Article
- Clinical and MRI factors influencing interobserver delineation variability of brain metastases before stereotactic radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Article
- Deep Learning-Based Objective Quantification of Nasopharyngeal Endoscopic Findings for Standardized Assessment of Inflammation.Diagnostics (Basel, Switzerland) · 2026Article
- Diagnostic Imaging of Pancreatic and Biliary Involvement in IgG4-Related Disease: Key Imaging Features, Diagnostic Criteria and Differential Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- Classifying diagnostic pitfalls in juxtapleural CT lesions: A root cause.European journal of radiology open · 2026Article
- Validation of an automated AI-based micro-CT organ segmentation workflow against expert annotations and its impact on fluorescence quantification.European radiology experimental · 2026Article
- Carotid Plaque Characteristics Evaluation on DUS and MDCTA: Interobserver and Intermodality Agreement in a Single-Center Study.Medicina (Kaunas, Lithuania) · 2026Article
- BrainGraphNet-ViT model: a hybrid model combining vision transformers and graph convolutional networks for brain tumor diagnosis.Scientific reports · 2026Article
- Comparative evaluation of large language model-based AI platforms for radiographic assessment in rheumatoid arthritis.Rheumatology international · 2026Article
- RetinaDetachNet: Automated Deep Learning Quantification of Photoreceptor Cell Death for Neuroprotection Studies in Experimental Retinal Detachment.Translational vision science & technology · 2026Article
- Point-of-Care Ultrasound in Airway Management.Journal of clinical medicine · 2026Review
- Interobserver variability of recall decisions between mammography readers in the English NHS breast screening programme: A comparison of interobserver variability measures.European journal of radiology · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo review the methodology of interobserver variability studies; including current practice and quality of conducting and reporting studies.
methodsInterobserver variability studies between January 2019 and January 2020 were included; extracted data comprised of study characteristics, populations, variability measures, key results, and conclusions. Risk of bias was assessed using the COSMIN tool for assessing reliability and measurement error.
resultsSeventy-nine full-text studies were included covering various imaging tests and clinical areas. The median number of patients was 47 (IQR:23-88), and observers were 4 (IQR:2-7), with sample size justified in 12 (15%) studies. Most studies used static images (
conclusionsInterobserver variability studies have diverse study designs and methods, the impact of which requires further evaluation. Sample size for patients and observers was often small without justification. Most studies report ICC and κ values, which did not always coincide with the study conclusion. High ratings were assigned to many studies using the COSMIN risk of bias tool, with certain standards scored 'not applicable' when static images were used. ADVANCES IN KNOWLEDGE: The sample size for both patients and observers was often small without justification. For most studies, observers interpreted static images and did not evaluate the process of acquiring the imaging test, meaning it was not possible to assess many COSMIN risk of bias standards for studies with this design. Most studies reported intraclass correlation coefficient and κ statistics; study conclusions often did not correspond with results.
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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.