Development of a Diagnostic Checklist to Identify Functional Cognitive Disorder Versus Other Neurocognitive Disorders
BMJ neurology open（2025）
Centre for Clinical Brain Sciences
Abstract
Background Functional cognitive disorder (FCD) poses a diagnostic challenge due to its resemblance to other neurocognitive disorders and limited biomarker accuracy. We aimed to develop a new diagnostic checklist to identify FCD versus other neurocognitive disorders.Methods The clinical checklist was developed through mixed methods: (1) a literature review, (2) a three-round Delphi study with 45 clinicians from 12 countries and (3) a pilot discriminative accuracy study in consecutive patients attending seven memory services across the UK. Items gathering consensus were incorporated into a pilot checklist. Item redundancy was evaluated with phi coefficients. A briefer checklist was produced by removing items with >10% missing data. Internal validity was tested using Cronbach’s alpha. Optimal cut-off scores were determined using receiver operating characteristic curve analysis.Results A full 11-item checklist and a 7-item briefer checklist were produced. Overall, 239 patients (143 FCD, 96 non-FCD diagnoses) were included. The checklist scores were significantly different across subgroups (FCD and other neurocognitive disorders) (F(2, 236)=313.3, p<0.001). The area under the curve was excellent for both the full checklist (0.97, 95% CI 0.95 to 0.99) and its brief version (0.96, 95% CI 0.93 to 0.98). Optimal cut-off scores corresponded to a specificity of 97% and positive predictive value of 91% for identifying FCD. Both versions showed good internal validity (>0.80).Conclusions This pilot study shows that a brief clinical checklist may serve as a quick complementary tool to differentiate patients with neurodegeneration from those with FCD. Prospective blind large-scale validation in diverse populations is warranted.Cite Now
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined