Background: Previous studies have shown that a single Coma-Recovery Scale-Revision (CRS-R) assessment can identify high rates of misdiagnosis by clinical consensus. The aim of this study was to investigate the proportion of misdiagnosis by clinical consensus compared to repeated behavior-scale assessments in patients with prolonged disorders of consciousness (DOC). Methods: Patients with prolonged DOC during hospitalization were screened by clinicians, and the clinicians formed a clinical-consensus diagnosis. Trained professionals used the CRS-R to evaluate the consciousness levels of the enrolled patients repeatedly (≥5 times) within a week. Based on the repeated evaluation results, the enrolled patients with prolonged DOC were divided into unresponsive wakefulness syndrome (UWS), minimally conscious state (MCS), and emergence from MCS (EMCS). Finally, the relationship between the results of the CRS-R and the clinical consensus were analyzed. Results: In this study, 137 patients with a clinical-consensus diagnosis of prolonged DOC were enrolled. It was found that 24.7% of patients with clinical UWS were actually in MCS after a single CRS-R behavior evaluation, while the repeated CRS-R evaluation results showed that the proportion of misdiagnosis of MCS was 38.2%. A total of 16.7% of EMCS patients were misdiagnosed with clinical MCS, and 1.1% of EMCS patients were misdiagnosed with clinical UWS. Conclusions: The rate of the misdiagnosis by clinical consensus is still relatively high. Therefore, clinicians should be aware of the importance of the bedside CRS-R behavior assessment and should apply the CRS-R tool in daily procedures. Trial registration: ClinicalTrials.gov ID: NCT04139239; Registered 24 October 2019 - Retrospectively registered.

Background: Burnout is more common among healthcare professionals, that is an important problem of professional distress that can seriously affect healthcare professionals’ emotional state, health, medical quality and doctor-patient relationship. However, only few studies researched the burnout status of healthcare professionals who care for patients with prolonged disorders of consciousness. The aim of this study was to evaluate the level of burnout and related contributing personal and environment factors in healthcare professionals managing these patients. Methods: Institution-based cross-sectional study. Maslach Burnout Inventory-Human Services Survey was used to evaluate burnout in professionals who specially care for patients with prolonged disorders of consciousness in the neurorehabilitation department. Results: A total of 200 questionnaires were distributed, 121 were collected, among them 93 questionnaires could be used for further analysis. In this study, 61 participants (65.6%) showed burnout (55.2% physicians and 82.9% nurses). For the risks and Maslach Burnout Inventory scores, emotional exhaustion and depersonalization were correlated with age, gender, occupation, marital status, years of practice, and education level. Reduced personal accomplishment was correlated with marital status. The variables of age (< 29 years old), occupation (nurses), marital status (unmarried), years of practice (< 5 years), and educational level (≤ Undergraduate) were associated with high levels of burnout. Conclusions: Healthcare professionals who care for patients with disorders of consciousness experienced high levels of burnout. Especially those who were younger, nurse, unmarried, less practice experience or lower educational levels were more likely to experience high burnout.

This study aims to validate the Chinese version of the Coma Recovery Scale-Revised (CRS-R). One hundred sixty-nine patients were assessed with both the CRS-R and the Glasgow Coma Scale (GCS), diagnosed as being in unresponsive wakefulness syndrome (UWS, formerly known as vegetative state), minimally conscious state (MCS), or emergence from MCS (EMCS). A subgroup of 50 patients has been assessed twice by the same rater, within 24 h. Patient outcome was documented six months after assessment. The internal consistency for the CRS-R total score was excellent (Cronbach's α = 0.84). Good test-retest reliability was obtained for CRS-R total score and subscale scores (intra-class correlation coefficient [ICC] = 0.87 and ICC = 0.66-0.84, respectively). Inter-rater reliability was high (ICC = 0.719; p < 0.01). Concurrent validity was good between CRS-R total scale and GCS total scale. Diagnostic validity was excellent compared with GCS (emerged from UWS: 24%; emerged from MCS: 28%). When considering patient outcome, diagnostic validity was good. In addition, false-positive rates have been detected for both diagnoses. The Chinese version of the CRS-R is a reliable and sensitive tool and can discriminate patients in UWS, MCS, and EMCS successfully.

Artificial intelligence (AI)-assisted PET imaging is emerging as a promising tool for the diagnosis of Parkinson’s disease (PD). We aim to systematically review the diagnostic accuracy of AI-assisted PET in detecting PD. The Ovid MEDLINE, Ovid Embase, Web of Science, and IEEE Xplore databases were systematically searched for related studies that developed an AI algorithm in PET imaging for diagnostic performance from PD and were published by August 17, 2023. Binary diagnostic accuracy data were extracted for meta-analysis to derive outcomes of interest: area under the curve (AUC). 23 eligible studies provided sufficient data to construct contingency tables that allowed the calculation of diagnostic accuracy. Specifically, 11 studies were identified that distinguished PD from normal control, with a pooled AUC of 0.96 (95% CI: 0.94–0.97) for presynaptic dopamine (DA) and 0.90 (95% CI: 0.87–0.93) for glucose metabolism (¹⁸F-FDG). 13 studies were identified that distinguished PD from the atypical parkinsonism (AP), with a pooled AUC of 0.93 (95% CI: 0.91 − 0.95) for presynaptic DA, 0.79 (95% CI: 0.75–0.82) for postsynaptic DA, and 0.97 (95% CI: 0.96–0.99) for ¹⁸F-FDG. Acceptable diagnostic performance of PD with AI algorithms-assisted PET imaging was highlighted across the subgroups. More rigorous reporting standards that take into account the unique challenges of AI research could improve future studies.

Behavioral assessment has been acted as the gold standard for the diagnosis of disorders of consciousness (DOC) patients. The item “Functional Object Use” in the motor function sub-scale in the Coma Recovery Scale-Revised (CRS-R) is a key item in differentiating between minimally conscious state (MCS) and emergence from MCS (EMCS). However, previous studies suggested that certain specific stimuli, especially something self-relevant can affect DOC patients’ scores of behavioral assessment scale. So, we attempted to find out if personalized objects can improve the diagnosis of EMCS in the assessment of Functional Object Use by comparing the use of patients’ favorite objects and other common objects in MCS patients. Twenty-one post-comatose patients diagnosed as MCS were prospectively included. The item “Functional Object Use” was assessed by using personalized objects (e.g., cigarette, paper) and non-personalized objects, which were presented in a random order. The rest assessments were performed following the standard protocol of the CRS-R. The differences between functional uses of the two types of objects were analyzed by the McNemar test. The incidence of Functional Object Use was significantly higher using personalized objects than non-personalized objects in the CRS-R. Five out of the 21 MCS studied patients, who were assessed with non-personalized objects, were re-diagnosed as EMCS with personalized objects (χ2 = 5, df = 1, p < 0.05). Personalized objects employed here seem to be more effective to elicit patients’ responses as compared to non-personalized objects during the assessment of Functional Object Use in DOC patients. Clinical Trials.gov: NCT02988206 ; Date of registration: 2016/12/12.

Modern facial surgical planning and therapeutic strategies rely heavily on the precise segmentation of the nasal cavity and paranasal sinuses from computed tomography (CT) images for quantitative analysis. Nevertheless, manual segmentation is labor-intensive and prone to inconsistencies, highlighting the need for automatic segmentation methods. A significant challenge in this field is the lack of publicly available clinical datasets for research. To address this issue, we introduce NagalSeg, the first large-scale, publicly available dataset for nasal cavity and paranasal sinus segmentation. In comparison to existing nasal structure segmentation datasets, which are either private or small-scale, NagalSeg stands out as the first publicly accessible dataset. It provides an order of magnitude more labeled data, consisting of 130 3D CT scans with pixel-wise annotations of five anatomical structures: the left nasal cavity, right nasal cavity, nasopharynx, left maxillary sinus, and right maxillary sinus. The NagalSeg dataset serves as an open-access resource to facilitate the development and evaluation of segmentation algorithms and promote future in-depth research towards the clinical application of artificial intelligence methods.