Expected-value bias in routine third-trimester growth scans.

OBJECTIVES: Operators performing fetal growth scans are usually aware of the gestational age of the pregnancy, which may lead to expected-value bias when performing biometric measurements. We aimed to evaluate the incidence of expected-value bias in routine fetal growth scans and assess its impact on standard biometric measurements. METHODS: We collected prospectively full-length video recordings of routine ultrasound growth scans coupled with operator eye tracking. Expected value was defined as the gestational age at the time of the scan, based on the estimated due date that was established at the dating scan. Expected-value bias was defined as occurring when the operator looked at the measurement box on the screen during the process of caliper adjustment before saving a measurement. We studied the three standard biometric planes on which measurements of head circumference (HC), abdominal circumference (AC) and femur length (FL) are obtained. We evaluated the incidence of expected-value bias and quantified the impact of biased measurements. RESULTS: We analyzed 272 third-trimester growth scans, performed by 16 operators, during which a total of 1409 measurements (354 HC, 703 AC and 352 FL; including repeat measurements) were obtained. Expected-value bias occurred in 91.4% of the saved standard biometric plane measurements (85.0% for HC, 92.9% for AC and 94.9% for FL). The operators were more likely to adjust the measurements towards the expected value than away from it (47.7% vs 19.7% of measurements; P < 0.001). On average, measurements were corrected by 2.3 ± 5.6, 2.4 ± 10.4 and 3.2 ± 10.4 days of gestation towards the expected gestational age for the HC, AC, and FL measurements, respectively. Additionally, we noted a statistically significant reduction in measurement variance once the operator was biased (P = 0.026). Comparing the lowest and highest possible estimated fetal weight (using the smallest and largest biased HC, AC and FL measurements), we noted that the discordance, in percentage terms, was 10.1% ± 6.5%, and that in 17% (95% CI, 12-21%) of the scans, the fetus could be considered as small-for-gestational age or appropriate-for-gestational age if using the smallest or largest possible measurements, respectively. Similarly, in 13% (95% CI, 9-16%) of scans, the fetus could be considered as large-for-gestational age or appropriate-for-gestational age if using the largest or smallest possible measurements, respectively. CONCLUSIONS: During routine third-trimester growth scans, expected-value bias frequently occurs and significantly changes standard biometric measurements obtained. © 2019 the Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology

Studies on Complement

This is a Dissertation submitted by Noble P. Sherwood.Non

Idealized Antenna Patterns for Use in Communication-satellite Interference Studies

Idealized antenna patterns for communication satellite interference studie

Comparative anatomy e-book with embedded quizzes: an active learning strategy

[Extract] The subject “Anatomy: structure and movement” is a first year anatomy course compulsory for Biomedical Science students and elective for Bachelor of Science students. This subject includes modules on both human and comparative vertebrate musculoskeletal systems (MSS). Whereas there is an excellent textbook for human MSS we have not found an adequate textbook that targets the key learning outcomes for the comparative vertebrate MSS module. We believe that this is one of the primary reasons that students struggle with this module, much more so than the human module, as borne out in the assessment data. Our aim was to create a targeted learning resource for the students and to encourage more independent and active learning through this resource

Alcohol-related expectancies are associated with the D2 dopamine receptor and GABAa receptor B3 subunit genes

Molecular genetic research has identified promising markers of alcohol dependence, including alleles of the D2 dopamine receptor (DRD2) and the GABAA receptor ￢3 subunit (GABRB3) genes. Whether such genetic risk manifests itself in stronger alcohol-related outcome expectancies, or in difficulty resisting alcohol, is unknown. In the present study, A1+ (A1A1 and A1A2 genotypes) and A1- (A2A2 genotype) alleles of the DRD2 and G1+ (G1G1 and G1 non-G1 genotypes) and G1- (non-G1 non-G1 genotype) alleles of the GABRB3 were determined in a group of 56 medically-ill patients diagnosed with alcohol dependence. Mood-related Alcohol Expectancy (AE) and Drinking Refusal Self-Efficacy (DRSE) were assessed using the Drinking Expectancy Profile (Young and Oei, 1996). Patients with the DRD2 A1+ allele, compared to those with the DRD2 A1- allele, reported lower DRSE in situations of social pressure (p=. 009). Similarly, lower DRSE was reported under social pressure by patients with the GABRB3 G1+ allele when compared to those with the GABRB3 G1- allele (p=.027). Patients with the GABRB3 G1+ allele also revealed reduced DRSE in situations characterized by negative affect than patients with the GABRB3 G1- alleles (p=. 037). Patients carrying the GABRB3 G1+ allele showed stronger AE relating to negative affective change (for example, increased depression) than their GABRB3 G1- counterparts (p=. 006). Biological influence in the development of some classes of cognitions is hypothesized. The clinical implications, particularly with regard to patient-treatment matching and the development of an integrated psychological and pharmacogenetic approach are discussed

Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation.

Recent automated medical image analysis methods have attained state-of-the-art performance but have relied on memory and compute-intensive deep learning models. Reducing model size without significant loss in performance metrics is crucial for time and memory-efficient automated image-based decision-making. Traditional deep learning based image analysis only uses expert knowledge in the form of manual annotations. Recently, there has been interest in introducing other forms of expert knowledge into deep learning architecture design. This is the approach considered in the paper where we propose to combine ultrasound video with point-of-gaze tracked for expert sonographers as they scan to train memory-efficient ultrasound image analysis models. Specifically we develop teacher-student knowledge transfer models for the exemplar task of frame classification for the fetal abdomen, head, and femur. The best performing memory-efficient models attain performance within 5% of conventional models that are 1000× larger in size