Recall Accuracy Research Articles

Investigating Creation Perspectives and Icon Placement Preferences for On-Body Menus in Virtual Reality

On-body menus present a novel interaction paradigm within Virtual Reality (VR) environments by embedding virtual interfaces directly onto the user’s body. Unlike traditional screen-based interfaces, on-body menus enable users to interact with virtual options or icons visually attached to their physical form. In this paper, We investigated the impact of the creation process on the effectiveness of on-body menus, comparing first-person, third-person, and mirror perspectives. Our first study ( N = 12) revealed that the mirror perspective led to faster creation times and more accurate recall compared to the other two perspectives. To further explore user preferences, we conducted a second study ( N = 18) utilizing a VR system with integrated body tracking. By combining distributions of icons from both studies ( N = 30), we confirmed significant preferences in on-body menu placement based on icon category ( e.g. , Social Media icons were consistently placed on forearms). We also discovered associations between categories, such as Leisure and Social Media icons frequently co-occurring. Our findings highlight the importance of the creation process, uncover user preferences for on-body menu organization, and provide insights to guide the development of intuitive and effective on-body interactions within virtual environments.

Deep Learning Method of Precious Wood Image Classification Based on Microscopic Computed Tomography

Correctly identifying precious wood species is crucial for import and export trade and furniture material identification. This study utilizes nondestructive testing (Microscopic Computed Tomography, Micro-CT) to capture microscopic images of the transverse, radial, and tangential sections of 24 precious wood species, creating a comprehensive dataset. The SLConNet deep learning model is developed, enhancing recognition accuracy through multi-scale convolution and an improved residual block structure. The experiment results show that the classification accuracy of the transverse, radial and tangential sections is 98.72, 96.75 and 95.36 % respectively when the gain value is 0.8. The model outperforms traditional models like Alexnet, ResNet50, Inception-V3, and Xception. This research highlights the efficiency of nondestructive testing in obtaining a large number of microscopic wood images, compared to traditional anatomical methods. The SLConNet model showcases high accuracy in precision, recall, and specificity, suggesting its potential for widespread applications in wood classification.

Reliability of Parental Recall of Birth Weight, Birth Length and Gestational Age in the Multicenter Cohort Study IDEFICS.

To investigate the reliability of parental recall of birth weight, birth length and gestational age several years after birth. Parentally recalled birth parameters were obtained from the European multicentric cohort study IDEFICS (Identification and prevention of dietary- and lifestyle-induced health effects in children and infants) and compared to the corresponding data externally recorded in the child's medical check-up booklet. The agreement between the two sources was examined using Bland-Altman plots, intraclass correlation coefficients and Cohen's kappa for clinically relevant categories. Additionally, logistic regression models were used to identify factors related to parental recall accuracy. A total of 4930 children aged 2 to 11years were included. Accuracy of birth weight within 100g was 88%, 94% of the recalled birth length was within 2cm, and 99% of the parents could recall with 2weeks accuracy how many weeks their child was delivered preterm. Discrepancies of more than two weeks or more than 100g were more likely in parents of low or medium socioeconomic status. Non-biological parents were 3.4 times more likely to have a discrepancy of more than 100g compared to biological mothers (95% CI 1.7-6.7). Moreover, parents were less likely to accurately recall their child's birth parameters with increasing number of children within a family. In general, parents' information matched well with the medical check-up booklet. However, accuracy varied among different groups which should be taken into consideration when using birth data recalled by parents in studies of child health.

Deep learning-based surgical step recognition for laparoscopic right-sided colectomy.

Understanding the complex anatomy and surgical steps involved in laparoscopic right-sided colectomy (LAP-RC) is essential for standardizing the surgical procedure. Deep-learning (DL)-based computer vision can achieve this. This study aimed to develop a step recognition model for LAP-RC using a dataset of surgical videos with annotated step information and evaluate its recognition performance. This single-center retrospective study utilized a video dataset of laparoscopic ileocecal resection (LAP-ICR) and laparoscopic right-sided hemicolectomy (LAP-RHC) for right-sided colon cancer performed between January 2018 and March 2022. The videos were split into still images, which were divided into training, validation, and test sets using 66%, 17%, and 17% of the data, respectively. Videos were manually classified into eight main steps: 1) medial mobilization, 2) central vascular ligation, 3) dissection of the superior mesenteric vein, 4) retroperitoneal mobilization, 5) lateral mobilization, 6) cranial mobilization, 7) mesocolon resection, and 8) intracorporeal anastomosis. In a simpler version, consecutive surgical steps were combined, resulting in five steps. Precision, recall, F1 scores, and overall accuracy were assessed to evaluate the model's performance in the surgical step classification task. Seventy-eight patients were included; LAP-ICR and LAP-RHC were performed in 35 (44%) and 44 (56%) patients, respectively. The overall accuracy was 72.1% and 82.9% for the eight-step and combined five-step classification tasks, respectively. The automatic surgical step-recognition model for LAP-RCs, developed using a DL algorithm, exhibited a fairly high classification performance. A model that understands the complex steps of LAP-RC will aid the standardization of the surgical procedure.

Rationale and Challenges for a New Instrument for Remote Measurement of Negative Symptoms

Abstract There is a broad consensus that the commonly used clinician administered rating scales for assessment of negative symptoms share significant limitations, including: 1) reliance upon accurate self-report and recall from the patient and caregiver; 2) potential for sampling bias and thus being unrepresentative of daily life experiences; 3) subjectivity of the symptom scoring process and limited sensitivity to change. These limitations led a work group from the International Society of CNS Clinical Trials and Methodology (ISCTM) to initiate the development of a multimodal negative symptom instrument. Experts from academia and industry reviewed the current methods of assessing the domains of negative symptoms including diminished (1) Affect; (2) Sociality; (3) Verbal communication; (4) Goal-directed behavior; and (5) Hedonic drives. For each domain they documented the limitations of the current methods and recommended new approaches that could potentially be included in a multimodal instrument. The recommended methods for assessing negative symptoms included ecological momentary assessment (EMA), in which the patient self-reports their condition upon receipt of periodic prompts from a smartphone or other device during their daily routine; and direct inference of negative symptoms through detection and analysis of the patient’s voice, appearance or activity from audio/visual or sensor-based (e.g., GPS, actigraphy) recordings captured by the patient’s smartphone or other device. The process for developing an instrument could resemble the NIMH MATRICS process that was used to develop a battery for measuring cognition in schizophrenia. Although the EMA and other digital measures for negative symptoms are at relatively early stages of development/maturity and development of such an instrument faces substantial challenges, none of them are insurmountable.

Eyewitness memory for person, object and action information is mediated by interview environment and the presence or absence of rapport

Purpose Obtaining accurate and reliable information from witnesses and victims of crime is essential for guiding criminal investigations and for the successful prosecution of offenders and beyond. Here, the purpose of this study is to investigate the impact of prosocial rapport behaviours and retrieval environment on mock eyewitness memory with an emphasis on the qualitative nature of information recalled in terms of persons, actions, objects and surroundings. Design/methodology/approach One hundred participants from the general population took part in mock witness research using a 2 (Environment: face-to-face; virtual) × 2 (Rapport: present; absent) design. Participants individually viewed an event depicting a fight in a bar and were then interviewed 48 h later according to condition. Findings Rapport and environment variously emerged as impactful, resulting in significant improvements in correct recall of persons, actions and object information when rapport was present and in virtual environments (VEs) when communicating via avatars. In many instances, the benefits of rapport and environment were additive, but not always. Surroundings information remained consistent across all conditions. Erroneous recall was extremely susceptible to inflation in face-to-face interviews when rapport was absent, in some instances increasing by over 100%. However, virtual interview spaces appeared to dilute the negative effects on retrieval when rapport was absent. Research limitations/implications This study concurs with others who have argued that comfortable witnesses are “better” witnesses; however, understanding what “comfort” looks and feels like remains a challenge. Practical implications The results, alongside the findings of others, have implications for applied and social cognition and reveal avenues for future research centred on widening access to justice and professional interview training. Originality/value This study reveals that prosocial rapport behaviours and VEs significantly enhance the accuracy of eyewitness recall. The findings suggest that virtual interviews can mitigate errors in the absence of rapport, offering valuable insights for criminal investigators and legal professionals. This research is crucial for improving witness interviewing techniques, thereby aiding in the accurate prosecution of offenders and broadening access to justice. The paper is valuable for criminal investigators, legal professionals, forensic psychologists and researchers focused on improving the reliability of eyewitness testimonies and interview methodologies.

Machine Learning Ensemble Methodologies for the Prediction of the Failure Mode of Reinforced Concrete Beam–Column Joints

One of the most critical aspects in the seismic behavior or reinforced concrete (RC) structures pertains to beam–column joints. Modern seismic design codes dictate that, if failure is to occur, then this should be the ductile yielding of the beam and not brittle shear failure of the joint, which can lead to sudden collapse and loss of human lives. To this end, it is imperative to be able to predict the failure mode of RC joints for a large number of structures in a building stock. In this research effort, various ensemble machine learning algorithms were employed to develop novel, robust classification models. A dataset comprising 486 measurements from real experiments was utilized. The performance of the employed classifiers was assessed using Precision, Recall, F1-Score, and overall Accuracy indices. N-fold cross-validation was employed to enhance generalization. Moreover, the obtained models were compared to the available engineering ones currently adopted by many international organizations and researchers. The novel ensemble models introduced in this research were proven to perform much better by improving the obtained accuracy by 12–18%. The obtained metrics also presented small variability among the examined failure modes, indicating unbiased models. Overall, the results indicate that the proposed methodologies can be confidently employed for the prediction of the failure mode of RC joints.

Effects of video game immersion and task interference on cognitive performance: a study on immediate and delayed recall and recognition accuracy.

This study investigates the cognitive impacts of video game immersion and task interference on immediate and delayed recall as well as recognition tasks. We enrolled 160 subjects aged 18 to 29, who were regular players of "shoot-em-up" video games for at least 3 years. Participants were assigned to one of three experimental groups or a control group. The experimental conditions varied in the timing and type of tasks: the first group performed a video game session between recall tasks, the second group multitasked with video games and recall tasks simultaneously, and the third group engaged in task switching from video games to recall tasks. Using the Rey Auditory Verbal Learning Test, we measured the effects of these conditions on cognitive performance, focusing on error types and recall accuracy. Results indicated that multitasking and task switching significantly affected the subjects' performance, with notable decrements in recall and recognition accuracy in conditions of high task interference. The study highlights the cognitive costs associated with multitasking in immersive digital games and provides insights into how task similarity and interference might increase error rates and affect memory performance.

Predicting the effectiveness of binaural beats on working memory.

Working memory is vital for short-term information processing. Binaural beats can enhance working memory by improving attention and memory consolidation through neural synchronization. However, individual differences in cognitive and neuronal functioning affect effectiveness of binaural beats, necessitating personalized approaches. This study aimed to develop a machine learning model to predict binaural beats's effectiveness on working memory using electroencephalography. Sixty healthy participants underwent a 5-min electroencephalography recording, an initial working memory evaluation, 15 min of binaural beats stimulation, and a subsequent working memory evaluation using digit span tests of increasing difficulty. Recall accuracy and response times were measured. Differential scores from pre-evaluation and post-evaluation labeled participants as active or inactive to binaural beats stimulation. electroencephalography data, recorded using 14 electrodes, provided brain activity estimates across theta, alpha, beta, and gamma frequency bands, resulting in 56 features (14 channels × 4 bands) for the machine learning model. Several classifiers were tested to identify the most effective model. The weighted K-nearest neighbors model achieved the highest accuracy (90.0%) and area under the receiver operating characteristic curve (92.24%). Frontal and parietal electroencephalography channels in theta and alpha bands were crucial for classification. This study's findings offer significant clinical insights, enabling informed interventions and preventing resource inefficiency.

Analysis of urinary non-formed components at home based on machine learning algorithms

Objective: Machine learning can automatically extract valuable insights from vast datasets, predict and classify diseases, and evaluate drug efficacy. To assess the effectiveness of machine learning algorithms in analyzing non-formed components in urine, real medical data were processed and annotated. Methods: Five models, including K-Nearest Neighbors, Decision Trees, Random Forests, Support Vector Machines, and Gaussian distributions, were constructed to quantitatively analyze 12 non-formed urine components, such as vitamin C, white blood cells, and urinary bilirubin. The efficacy of these models was then compared. Results: It was found that the Random Forest model outperformed others, achieving the lowest mean squared error, high recall rate, accuracy, and area under the curve. Conclusions: These findings indicate that machine learning offers significant potential for studying non-formed urine components, potentially enhancing the precision and effectiveness of disease detection and providing valuable support for clinical decision-making.

THE USE OF MNEMONIC METHODS AND ASSOCIATIVE CARDS IN STUDYING GENETICS ANNOTATION

This article explores the effectiveness of using mnemonic techniques and associative cards in the study of genetics. The focus is on the principles of how associative cards function and their role in enhancing the memorization of complex genetic terms and processes. The literature review supports the positive impact of visualization and associations on students’ cognitive activities, highlighting how these methods facilitate the retention of challenging information in fields like biology and genetics. Mnemonic techniques, which combine verbal and visual elements, help learners build stronger cognitive connections and improve recall accuracy. Associative cards are discussed as a tool for integrating visual and textual information, allowing students to grasp genetic concepts such as DNA replication, mutations, and hereditary patterns more effectively.Moreover, the article examines the practical implementation of mnemonic methods in the modern educational system, particularly focusing on the use of digital platforms like Quizlet and Anki. These platforms utilize spaced repetition techniques to improve long-term retention, making the learning process more flexible and interactive. The research emphasizes how personalized study approaches, using these cognitive tools, contribute to better learning outcomes and overall academic performance.The conclusions show that the incorporation of mnemonic techniques and associative cards into genetics education not only enhances student engagement but also significantly improves the efficiency of mastering complex biological concepts. This study underlines the potential of these methods to be further integrated into educational curricula to address learning challenges in genetic studies.

Abstract A065: How accurate are parents' recalls? Factors affecting the concordance of parental and provider reports of HPV vaccination among African American adolescents

Abstract Objectives. Since the introduction of the human papillomavirus (HPV) vaccine in 2006, substantial efforts have been made at the national and state levels to monitor its uptake and understand the factors influencing vaccination. Most studies have focused on the acceptability and intent to vaccinate among adolescents and their parents, providing limited insights into how this understanding affects actual vaccine uptake and completion. A common method for measuring adolescents' vaccination status is parental recall. While some research has assessed the accuracy of parental recall, its implications have not been thoroughly explored. African Americans are disproportionately affected by HPV-related cancers, with higher mortality rates for cervical and oropharyngeal cancers compared to non-Hispanic whites. This study examined the accuracy of parents' reports of their adolescent children's HPV vaccination status and how this accuracy varied by socio-demographic characteristics within the Black/African American community. Methods. Data were taken from the 2015–2019 National Immunization Survey (NIS)-Teen, which includes a household interview and a provider-completed immunization history. We evaluated concordance between parents’ and providers’ reports of teens’ HPV vaccine initiation (at least 1 dose) and completion (3 doses) among Black/African American adolescents between the age of 13 to 17. We assessed bivariate associations of sociodemographic characteristics with having a concordant, false-positive (overreporting) or false-negative (underreporting) report, and used multinomial logistic regression to estimate the independent impact of each characteristic. Results: In a cohort of 68,205 adolescents (51.2% male, 95% CI: 50.4%-51.8%; 48.8% female, 95% CI: 48.2%-49.5%), parental reporting discrepancies in vaccination status were observed. Girls' parents were more likely to underreport (β=0.10, p=0.03) and overreport (β=0.10, p=0.03) vaccination status compared to boys' parents. Mothers with higher education levels were less likely to underreport, while single mothers were more likely to underreport (β=0.15, p=0.02) and overreport (β=0.17, p=0.008) vaccination status. Conclusions: Discrepancies between physician-reported and parental-reported HPV vaccination records among African American teenagers were observed, with parental reporting accuracy varying based on the child's gender and the mother's education level and marital status. Strategies to improve parental reporting accuracy and address disparities are needed to increase HPV vaccination rates and reduce HPV-related diseases in the African American community. Citation Format: Elaine Z. Liu, Philip Siu, Chun Pan, Grace X. Ma, Lin Zhu. How accurate are parents' recalls? Factors affecting the concordance of parental and provider reports of HPV vaccination among African American adolescents [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A065.

A new injury prevention program ‘FUNBALL’ improves cognitive performance of young football (soccer) players: A cluster randomized controlled trial

The youth football injury prevention program ‘FUNBALL’ contains exercises requiring high cognitive demands, which are performed concurrently to the training of the respective motor task. This study evaluates whether the program increases cognitive performances of young football players.1253 football players (aged 13–19 years old) were randomly assigned to either a control (CON) or an intervention (INT) group. The INT group performed the ‘FUNBALL’ program at least twice per week in their training sessions for one season (9 months). The CON group continued their training routine. From the total sample, the cognitive performance of 304 players (n = 135 CON; n = 169 INT) was assessed at the beginning and the end of the season using the Cogstate® Brief Battery, with the following subtests: One Back test (accuracy), Two Back test (accuracy), One Card Learning test (accuracy), Chase Test (correct moves per second), Set Shifting (accuracy), Identification test (speed), Detection test (speed), Groton Maze Learning Test (accuracy), and Groton Maze Learning Test Delayed Recall (accuracy).A multivariate analysis of variance (MANOVA) on the cognitive performance improvements from pre-to posttest showed that the INT group improved their performances more strongly than the CON group for all cognitive tests, namely for working memory, visual learning, visual motor control, attention, psychomotor function, memory, and executive function.The present study indicates that the exercise-based injury prevention program ‘FUNBALL’ may improve the cognitive performance of young football players. Future research should include an active control group, and should investigate whether the improvement in cognition also has beneficial effects on in-game performance. Trial registration numberAsPredicted (https://aspredicted.org/2kb3b.pdf).

Spatial Memory of Notable Hurricane Tracks and Their Geophysical Hazards

Previous research has shown that people use a benchmark hurricane as part of their preparation and evacuation decision-making process. While hurricanes are a common occurrence along the Gulf Coast, research on personal memories of past storms is lacking. Particularly, how well do people remember the track and geophysical hazards (wind speed, storm surge, and total rainfall) of past storms? The accurate or inaccurate recollection and perception of previous storm details can influence personal responses to future storms, such as the decision to evacuate or take other life-saving actions. Survey responses of residents in Alabama and Mississippi were studied to determine if people were accurately able to recall a notable storm’s name when seeing an image of the storm’s track. Those who were able to identify the storm by its track were also asked if they could remember the storm’s maximum reported rainfall, maximum sustained winds, and storm surge at landfall. Results showed that there were statistically significant differences between the levels of accurate recall for different storms, with Hurricanes Katrina and Michael having the most correct responses. Regardless of the storm, most people struggled to remember geophysical hazards. The results of this study are important as they can inform broadcast meteorologists and emergency managers on forecast elements of the storm to better emphasize in future communication in comparison to the actual values from historical benchmark storms.

Story Recall in Peer Conflict Resolution Discourse Task to Identify Older Adults Testing Within Range of Cognitive Impairment.

The current study used behavioral measures of discourse complexity and story recall accuracy in an expository discourse task to distinguish older adults testing within range of cognitive impairment according to a standardized cognitive screening tool in a sample of self-reported healthy older adults. Seventy-three older adults who self-identified as healthy completed an expository discourse task and neuropsychological screener. Discourse data were used to classify participants testing within range of cognitive impairment using multiple machine learning algorithms and stability analysis for identifying reliably predictive features in an effort to maximize prediction accuracy. We hypothesized that a higher rate of pronoun use and lower scores on story recall would best classify older adults testing within range of cognitive impairment. The highest classification accuracy exploited a single variable in a remarkably intuitive way: using 66% story recall as a cutoff for cognitive impairment. Forcing this decision tree model to use more features or increasing its complexity did not improve accuracy. Permutation testing confirmed that the 77% accuracy and 0.18 Brier skill score achieved by the model were statistically significant (p < .00001). These results suggest that expository discourse tasks that place demands on executive functions, such as working memory, can be used to identify aging adults who test within range of cognitive impairment. Accurate representation of story elements in working memory is critical for coherent discourse. Our simple yet highly accurate predictive model of expository discourse provides a promising assessment for easy identification of cognitive impairment in older adults. https://doi.org/10.23641/asha.26543824.

Cost-Sensitive Rainfall Intensity Prediction with High-Noise Commercial Microwave Link Data

Rainfall intensity prediction based on commercial microwave link data has received significant attention in recent years due to the higher spatial resolution and lower energy consumption. However, the predictive performance is inferior to the model based on meteorological data by reason of the high noise in commercial microwave link data, further exacerbated by the imbalance in the number of samples across different rainfall intensities. Hence, a cost-sensitive rainfall intensity prediction model (CSRFP) is proposed to achieve better predictive performance in high-noise commercial microwave link data. First, the spatiotemporal scene information is encoded, and its weights are trained to provide the model with correlations between signal data from different stations, which helps the model to better capture potential patterns between the data and thus reduce the effect of noise. Next, the rainfall cross-entropy loss based on the rainfall distribution provides the model with the probability of different rainfall intensities occurring and back-calculates the signal attenuation at a specific rainfall intensity, assigning more reasonable weights to different samples considering signal attenuation, which makes the model cost-sensitive and can address the class imbalance problem. Extensive experiments are carried out on high-noise communication data and imbalanced rainfall data in Fuzhou. Compared to typical prediction methods such as RNN applied to rainfall and communication data, CSRFP improves Recall, Precision, AUCROC, AUCPR and F1 and Accuracy by approximately 19%, 37%, 8%, 22%, 30%, and 17%, respectively. Significantly, the model’s prediction accuracy for heavy rain with the smallest number of samples improves by about 13%.

Self-Attention Multiresolution Analysis-Based Informal Settlement Identification Using Remote Sensing Data

The global dilemma of informal settlements persists alongside the fast process of urbanization. Various methods for analyzing remotely sensed images to identify informal settlements using semantic segmentation have been extensively researched, resulting in the development of numerous supervised and unsupervised algorithms. Texture-based analysis is a topic extensively studied in the literature. However, it is important to note that approaches that do not utilize a multiresolution strategy are unable to take advantage of the fact that texture exists at different spatial scales. The capacity to do online mapping and precise segmentation on a vast scale while considering the diverse characteristics present in remotely sensed images carries significant consequences. This research presents a novel approach for identifying informal settlements using multiresolution analysis and self-attention techniques. The technique shows potential for being resilient in the presence of inherent variability in remotely sensed images due to its capacity to extract characteristics at many scales and prioritize areas that contain significant information. Segmented pictures underwent an accuracy assessment, where a comparison analysis was conducted based on metrics such as mean intersection over union, precision, recall, F-score, and overall accuracy. The proposed method’s robustness is demonstrated by comparing it to various state-of-the-art techniques. This comparison is conducted using remotely sensed images that have different spatial resolutions and informal settlement characteristics. The proposed method achieves a higher accuracy of approximately 95%, even when dealing with significantly different image characteristics.

Navigating the mind's eye: Understanding gaze shifts in visuospatial bootstrapping.

Visuospatial bootstrapping refers to the well-replicated phenomena in which serial recall in a purely verbal task is boosted by presenting digits within the familiar spatial layout of a typical telephone keypad. The visuospatial bootstrapping phenomena indicates that additional support comes from long-term knowledge of a fixed spatial pattern, and prior experimentation supports the idea that access to this benefit depends on the availability of the visuospatial motor system. We investigate this by tracking participants' eye movements during encoding and retention of verbal lists to learn whether gaze patterns support verbal memory differently when verbal information is presented in the familiar visual layout. Participants' gaze was recorded during attempts to recall lists of seven digits in three formats: centre of the screen, typical telephone keypad, or a spatially identical layout with randomised number placement. Performance was better with the typical than with the novel layout. Our data show that eye movements differ when encoding and retaining verbal information that has a familiar layout compared with the same verbal information presented in a novel layout, suggesting recruitment of different spatial rehearsal strategies. However, no clear link between gaze pattern and recall accuracy was observed, which suggests that gazes play a limited role in retention, at best.

The relationship between recall accuracy and the use of gestures according to age and word meaning structure

The aim of this study was to explore differences in gesture use during a free recall task among individuals of different ages and different development of conceptual thinking. Sixty-three children (aged 7–14 years) and 30 adults participated in an experiment in which they first witnessed a live staged event and a week later were interviewed about the event and tested using the Word Meaning Structure Test. Results showed that adults used more gesture types and showed higher gesture use frequency than children. Further, we found that word meaning structure and gesture use – specifically metaphoric and iconic gestures – predicted more accurate free recall in children. Our findings suggest that gestures may function differently during varying developmental stages, with gesture use being related to word meaning structure and the benefits of gesture use in free recall being more evident in children than adults.

An Analysis of the Structures People With Shoulder Pain Recall After Receiving Their Diagnostic Imaging Results.

Rotator cuff related shoulder pain (RCRSP) is a prevalent cause of musculoskeletal pain. Patients presenting with this condition often undergo diagnostic imaging. However, many patients appear to have difficulty recalling the nature of their diagnosis. This may impact their rehabilitation outcomes. The aim of this study was to explore peoples' recall of their structural features relating to RCRSP by comparing their recalled description of their condition with the contents of their imaging reports as reported in an online questionnaire. In doing so, we aim to explore the potential emphasis they place on structural features related to their condition. This study was a content analysis embedded within a larger survey study published previously. Data containing patients' recall of their diagnosis was used from the study, and analysed using content analysis to quantify content in terms of codes and categories. The aims of frequency, completeness and accuracy of recall were then explored. The analysis identified eight categories of terminology either recalled by patients or reported in imaging reports. The most frequent categories in reports were tendinopathy (71%) and joint (67%), but only 17% and 36% of participant responses contained these codes, respectively. Participants' completeness of recall compared with their imaging report was 30% on average. In terms of accuracy, only 8 out of 95 participants recalled their diagnoses 100% correctly. This study indicates that patients have poor overall recall of their RCRSP-related structural features. We contend that participants may have been able to recall what they viewed to be the most prominent structural feature in their imaging findings. It may be important for clinicians to consider the impact of diagnostic labelling, and whether a patient's ability to recall such labelling may have an influence on their outcomes.