PURPOSE: To demonstrate automatic detection of BM on three MRI datasets using a deep learning-based approach. To improve the performance of the network is iteratively co-trained with datasets from different domains. A systematic approach is proposed to prevent catastrophic forgetting during co-training.
STUDY TYPE: Retrospective.
POPULATION: A total of 156 patients (105 ground truth and 51 pseudo labels) with 1502 BM (BrainMetShare); 121 patients with 722 BM (local); 400 patients with 447 primary gliomas (BrATS). Training/pseudo labels/validation data were distributed 84/51/21 (BrainMetShare). Training/validation data were split: 121/23 (local) and 375/25 (BrATS).
FIELD STRENGTH/SEQUENCE: A 5 T and 3 T/T1 spin-echo postcontrast (T1-gradient echo) (BrainMetShare), 3 T/T1 magnetization prepared rapid acquisition gradient echo postcontrast (T1-MPRAGE) (local), 0.5 T, 1 T, and 1.16 T/T1-weighted-fluid-attenuated inversion recovery (T1-FLAIR) (BrATS).
ASSESSMENT: The ground truth was manually segmented by two (BrainMetShare) and four (BrATS) radiologists and manually annotated by one (local) radiologist. Confidence and volume based domain adaptation (CAVEAT) method of co-training the three datasets on a 3D nonlocal convolutional neural network (CNN) architecture was implemented to detect BM.
STATISTICAL TESTS: The performance was evaluated using sensitivity and false positive rates per patient (FP/patient) and free receiver operating characteristic (FROC) analysis at seven predefined (1/8, 1/4, 1/2, 1, 2, 4, and 8) FPs per scan.
RESULTS: The sensitivity and FP/patient from a held-out set registered 0.811 at 2.952 FP/patient (BrainMetShare), 0.74 at 3.130 (local), and 0.723 at 2.240 (BrATS) using the CAVEAT approach with lesions as small as 1 mm being detected.
DATA CONCLUSION: Improved sensitivities at lower FP can be achieved by co-training datasets via the CAVEAT paradigm to address the problem of data sparsity.
LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.
METHODS: All undergraduate second-year dental students were invited to participate in the dental materials science practical session and were randomly allocated into two groups: Group 1 (feedback) sandwich and Group 2 (ATA). The session began with the teacher giving a short briefing on the commonly used dental materials, followed by a short demonstration of the manipulation of those materials. Students were then allowed to mix and manipulate the materials, and teachers provided feedback accordingly. At the end of the session, 16 close-ended (five-point Likert scales) and an open-ended questionnaire were distributed to students to evaluate their perceptions of the feedback given. Internal reliability of the questionnaire items was evaluated using Cronbach's alpha. Mean feedback scores were analysed using an independent t-test with ANCOVA for controlling gender and ethnicity. Thematic analysis was used to code the qualitative data.
RESULTS: Sixty-nine students participated in the present study with the majority being females (72.5%) and Chinese (79.7%). Cronbach's alpha analysis suggested removing three Likert-scale items, with the remaining 13 items being accepted. Generally, no significant difference was noted between the two groups (p = 0.197), but three items were found to be significant (p
METHODS: A qualitative study was conducted among patients and primary care trainees (known henceforth as doctors). Patients aged ≥ 60 years, having ≥ 1 chronic disease and prescribed ≥ 5 medications and could communicate in either English or Malay were recruited. Doctors and patients were purposively sampled based on their stage of training as family medicine specialists and ethnicity, respectively. All interviews were audio-recorded and transcribed verbatim. A thematic approach was used to analyse data.
RESULTS: Twenty-four in-depth interviews (IDIs) with patients and four focus group discussions (FGDs) with 23 doctors were conducted. Four themes emerged: understanding the concept of deprescribing, the necessity to perform deprescribing, concerns regarding deprescribing and factors influencing deprescribing. Patients were receptive to the idea of deprescribing when the term was explained to them, whilst doctors had a good understanding of deprescribing. Both patients and doctors would deprescribe when the necessity outweighed their concerns. Factors that influenced deprescribing were doctor-patient rapport, health literacy among patients, external influences from carers and social media, and system challenges.
CONCLUSION: Deprescribing was deemed necessary by both patients and doctors when there was a reason to do so. However, both doctors and patients were afraid to deprescribe as they 'didn't want to rock the boat'. Early-career doctors were reluctant to deprescribe as they felt compelled to continue medications that were initiated by another specialist. Doctors requested more training on how to deprescribe medications.
OBJECTIVE: This research aims to determine factors influencing students' behavioural intention to use Rain Classroom.
METHODS: In this cross-sectional and correlational investigation, 1138 medical students from five medical universities in Guangxi Province, China, made up the sample. This study added self-efficacy (SE), motivation (MO), stress (ST), and anxiety (AN) to the UTAUT framework. This study modified the framework by excluding actual usage variables and focusing only on intention determinants. SPSS-26 and AMOS-26 were used to analyze the data. The structural equation modelling technique was chosen to confirm the hypotheses.
RESULTS: Except for facilitating conditions (FC), all proposed factors, including performance expectancy (PE), effort expectancy (EE), social influence (SI), self-efficacy (SE), motivation (MO), anxiety (AN), and stress (ST), had a significant effect on students' behavioural intentions to use Rain Classroom.
CONCLUSIONS: The research revealed that the proposed model, which was based on the UTAUT, is excellent at identifying the variables that influence students' behavioural intentions in the Rain Classroom. Higher education institutions can plan and implement productive classrooms.
METHODS: This was a cross-sectional, questionnaire-based study conducted in a public primary care setting in Singapore. A total of 289 participants aged 21-80 years with hypertension were recruited. Self-care profiles were measured using the Hypertension Self-Care Profile (HTN-SCP; range 0-240, domain range 0-80). Health literacy was measured using the Short-Form Health Literacy Scale (HLS-SF12; range 0-50, limited literacy ≤33).
RESULTS: The mean self-care score was 182.7 (standard deviation [SD] 23.2). The median HL score was 34.7 (interquartile range [IQR] 31.9-40.3), and 31.1% of participants had limited HL. Self-care was not associated with age, CKD status, household income and education, but was associated with gender and HL score. In the final regression model, lower HL scores (adjusted β = 1.03, 95% confidence interval [CI] 0.7 to 1.36, P < 0.001) and male gender (adjusted β = -5.29, 95% CI -10.56 to -0.03, P = 0.049) were associated with lower self-care scores. The HL scores were associated with self-care domains of self-efficacy (HL: β = 0.30, 95% CI 0.17 to 0.42, P < 0.001), motivation (HL: β = 0.40, 95% CI 0.26 to 0.53, P < 0.001) and behaviour (HL: β = 0.38, 95% CI 0.26 to 0.50, P < 0.001).
CONCLUSION: Thirty-one percent of the participants had limited HL. Self-care was not associated with age, race, CKD status, household income or education. Male gender and limited HL were associated with lower self-care. Self-care was associated with self-efficacy, motivation and behaviour. Future research could focus on more targeted approaches to improve self-care and HL among patients with CKD.