Omission Errors Research Articles

Speech sound errors in adolescents with congenital hearing loss

Children with hearing impairment (HI) often have vowel and consonant errors. Speech-language pathologists need this information to correct articulation errors. Despite the prevalence of research on speech sound error patterns in hearing aid users, there is a lack of literature in the adolescent group. The present study aimed to investigate the common speech sound errors in HI adolescents from the Malayalam-speaking community who have severe to profound hearing loss and are digital hearing aid users. A total of 15 monolingual Malayalam-speaking children between the ages of 9 and 16 years were recruited who had been diagnosed with speech sound disorders secondary to congenital hearing impairment. The minimum language age for inclusion in the study was over three years. Articulatory error analysis was performed for all consonants and vowels of the Malayalam language, and frequently produced vowel and consonant errors were included in the SODA and PMV analyses. The consonants frequently produced speech sound errors and no vowel errors were found. The most commonly produced consonant errors were fricatives, trills, laterals, velars, and alveolars. Substitution and omission errors were the most common type of error. Place and manner errors were more common than other types of errors. The results of this study may help parents and rehabilitation professionals to understand the common errors that persist even after intensive intervention. It may also be a useful guide for AAC therapists and speech-language pathologists in setting appropriate speech production goals for adolescents with HI.

Study on the Method of Vineyard Information Extraction Based on Spectral and Texture Features of GF-6 Satellite Imagery

Accurately identifying the distribution of vineyard cultivation is of great significance for the development of the grape industry and the optimization of planting structures. Traditional remote sensing techniques for vineyard identification primarily depend on machine learning algorithms based on spectral features. However, the spectral reflectance similarities between grapevines and other orchard vegetation lead to persistent misclassification and omission errors across various machine learning algorithms. As a perennial vine plant, grapes are cultivated using trellis systems, displaying regular row spacing and distinctive strip-like texture patterns in high-resolution satellite imagery. This study selected the main oasis area of Turpan City in Xinjiang, China, as the research area. First, this study extracted both spectral and texture features based on GF-6 satellite imagery, subsequently employing the Boruta algorithm to discern the relative significance of these remote sensing features. Then, this study constructed vineyard information extraction models by integrating spectral and texture features, using machine learning algorithms including Naive Bayes (NB), Support Vector Machines (SVMs), and Random Forests (RFs). The efficacy of various machine learning algorithms and remote sensing features in extracting vineyard information was subsequently evaluated and compared. The results indicate that three spectral features and five texture features under a 7 × 7 window have significant sensitivity to vineyard recognition. These spectral features include the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Water Index (NDWI), while texture features include contrast statistics in the near-infrared band (B4_CO) and the variance statistic, contrast statistic, heterogeneity statistic, and correlation statistic derived from NDVI images (NDVI_VA, NDVI_CO, NDVI_DI, and NDVI_COR). The RF algorithm significantly outperforms both the NB and SVM models in extracting vineyard information, boasting an impressive accuracy of 93.89% and a Kappa coefficient of 0.89. This marks a 12.25% increase in accuracy and a 0.11 increment in the Kappa coefficient over the NB model, as well as an 8.02% enhancement in accuracy and a 0.06 rise in the Kappa coefficient compared to the SVM model. Moreover, the RF model, which amalgamates spectral and texture features, exhibits a notable 13.59% increase in accuracy versus the spectral-only model and a 14.92% improvement over the texture-only model. This underscores the efficacy of the RF model in harnessing the spectral and textural attributes of GF-6 imagery for the precise extraction of vineyard data, offering valuable theoretical and methodological insights for future vineyard identification and information retrieval efforts.

Clinical Utility of Virtual Kitchen Errand Task for Children (VKET-C) as a Functional Cognition Evaluation for Children with Developmental Disabilities

Background/Objectives: This study evaluated the clinical utility of a virtual reality (VR)-based kitchen error task for children (VKET-C) to assess functional cognition in children. Methods: In total, 38 children aged 7–12 years were included, comprising 23 typically developing (TD) children and 15 children with developmental disabilities (DDs), including autism spectrum disorder, attention deficit hyperactivity disorder, and intellectual disability. While performing the VKET-C, performance errors were analyzed. The Stockings of Cambridge (SOC) and Spatial Working Memory (SWM) tasks from the Cambridge Neuropsychological Test Automated Battery (CANTAB) were used to assess cognitive function. The Brunner–Munzel test was performed to compare performance errors between the TD and DD groups, and correlations between performance errors and cognitive measures were analyzed. Results: Omission and commission errors were significantly different between the groups (p < 0.001), with no significant difference in motor errors (p > 0.05). Omission errors were correlated with the initial thinking time mean (ITMN) in all items of the SOC task and the between errors (BE) of the SWM task. Commission errors were correlated with the ITMN in the difficult items of the SOC task and the BE of the SWM task. Additionally, motor errors were significantly correlated with problems solved in minimum moves (PSMM) and ITMN in the difficult items of the SOC task and BE in the SWM task. Conclusions: The VKET-C shows promise as an effective tool for assessing executive function and working memory in children with DDs, offering an engaging and ecologically valid alternative to traditional methods.

Accuracy Assessment of NOAA IMS 4 km Products on the Tibetan Plateau with Landsat-8 OLI Images

The NOAA IMS (Interactive Multisensor Snow and Ice Mapping System) is a blended snow and ice product based on active and passive satellite sensors, ground observation, and other auxiliary information, providing the daily cloud-free snow cover extent in the Northern Hemisphere (NH) and having great application potential in snow cover monitoring and research in the Tibetan Plateau (TP). However, accuracy assessment of products is crucial for various aspects of applications. In this study, Landsat-8 OLI images were used to evaluate and validate the accuracy of IMS products in snow cover monitoring on the TP. The results show that (1) average overall accuracy of IMS 4 km products is 76.0% and average mapping accuracy is 88.3%, indicating that IMS 4 km products are appropriate for large-scale snow cover monitoring on the TP. (2) IMS 4 km products tend to overestimate actual snow cover on the TP, with an average commission rate of 45.4% and omission rate of 11.7%, and generally present that the higher the proportion of snow-covered area, the lower the probability of omission rate and the higher the probability of commission rate. (3) Mapping accuracy of IMS 4 km snow cover on the TP generally is higher at the high altitudes, and commission and omission errors increase with the decrease of elevation. (4) Compared with less regional representativeness of ground observations, the spatial characteristics of snow cover based on high-resolution remote sensing data are much more detailed, and more reliable verification results can be obtained. (5) In addition to commission and omission error metrics, the overall accuracy and mapping accuracy based on the reference image instead of classified image can better reveal the general monitoring accuracy of IMS 4 km products on the TP area.

Analysis of the Biennial Productivity of Arabica Coffee with Google Earth Engine in the Northeast Region of São Paulo, Brazil

Numerous challenges are associated with the classification of satellite images of coffee plantations. The spectral similarity with other types of land use, variations in altitude, topography, production system (shaded and sun), and the change in spectral signature throughout the phenological cycle are examples that affect the process. This research investigates the influence of biennial Arabica coffee productivity on the accuracy of Landsat-8 image classification. The Google Earth Engine (GEE) platform and the Random Forest algorithm were used to process the annual and biennial mosaics of the Média Mogiana Region, São Paulo (Brazil), from 2017 to 2023. The parameters evaluated were the general hits of the seven classes of land use and coffee errors of commission and omission. It was found that the seasonality of the plant and its development phases were fundamental in the quality of coffee classification. The use of biennial mosaics, with Landsat-8 images, Brightness, Greenness, Wetness, SRTM data (elevation, aspect, slope), and LST data (Land Surface Temperature) also contributed to improving the process, generating a classification accuracy of 88.8% and reducing coffee omission errors to 22%.

Improved phenology-based rice mapping algorithm by integrating optical and radar data

Information on rice planting areas is critically important for food and water security, as well as for adapting to climate change. Mapping rice globally remains challenging due to the diverse climatic conditions and various rice cropping systems worldwide. Synthetic Aperture Radar (SAR) data, which is immune to climatic conditions, plays a vital role in rice mapping in cloudy, rainy, low-latitude regions but it suffers from commission errors in high-latitude regions. Conversely, optical data performs well in high-latitude regions due to its high observation frequency and less cloud contamination but faces significant omission errors in low-latitude regions. An effective integrated method that combines both data types is key to global rice mapping. Here, we propose a novel adaptive rice mapping framework named Rice-Sentinel that combines Sentinel-1 and Sentinel-2 data. First, we extracted key phenological phases of rice (e.g., the flooding and transplanting phase and the rapid growth phase), by analyzing the characteristic V-shaped changes in the Sentinel-1 VH curve. Second, we identified potential flooding signals in rice pixels by integrating the VH time series from Sentinel-1 with the Land Surface Water Index (LSWI) and Enhanced Vegetation Index (EVI) from Sentinel-2, utilizing the generated phenology phases. Third, the rapid growth signals of rice following its flooding phase were identified using Sentinel-2 data. Finally, rice fields were identified by integrating flooding and rapid growth signals. The resultant rice maps in six different case regions of the world (Northeast and South China, California, USA, Mekong Delta of Vietnam, Sakata City in Japan, and Mali in Africa) showed overall accuracies over 90 % and F1 scores over 0.91, outperforming the existing methods and products. This algorithm combines the strengths of both optical and SAR time series data and leverages biophysical principles to generate robust rice maps without relying on any prior ground truth samples. It is well-positioned for global applications and is expected to contribute to global rice monitoring efforts.

Super-resolution cropland mapping with Sentinel-2 images based on a self-training learning network

ABSTRACT The Sentinel-2 image is widely used for cropland mapping, but the limitation of its 10 m spatial resolution may significantly impact the accuracy of result in areas characterized by severe cropland fragmentation. To address the issue, this letter proposes a super-resolution cropland mapping model for Sentinel-2 images. The proposed model improves the spatial resolution of Sentinel-2 images to 2.5 m through self-training learning with the Swin Transformer for Image Restoration (SwinIR) model without needing fine-resolution training samples. Then, the random forest classification is applied to map cropland from the super-solved 2.5 m resolution image. The proposed model was assessed in the Jianghan Plain, China, and the results show that the super-resolution images produce cropland maps with higher accuracy than the original Sentinel-2 images. Comparing cropland mapping results before and after super-resolution, the overall accuracy improves from 0.82 to 0.89, while the commission error and omission error decrease from 0.09 to 0.05 and 0.17 to 0.11, respectively. This method addresses the shortage of training samples collection and improves the accuracy of cropland mapping, contributing to more reliable agricultural remote sensing applications.

Distributed Ledger Technology and Financial Reporting Integrity in Nigerian Quoted Banks: A Study on Error Reduction and Enhanced Transparency

This study investigated the effect of distributed ledger technology (DLT) factors on eliminating financial reporting errors (FREs) in quoted Nigerian banks. Using an exploratory survey design, data was collected from 300 employees of 14 quoted banks involved in financial reporting. DLT factors of public, private, hybrid, and blockchain were examined as independent variables affecting the dependent variable of FRE elimination. Descriptive analysis showed that all DLT types were perceived as highly effective for error reduction. Correlation analysis revealed strong positive relationships between DLT factors and FRE mitigation. Regression modeling found that hybrid DLT had the largest impact on error elimination, followed by private, public, and blockchain DLT. Together, the DLT factors explained 98.1% of the variance in FRE reduction. The results statistically established the significant positive effects of DLT factors on eliminating prevalent FREs like principle, omission, entry, disclosure, and reversal errors. Key contributions include providing robust empirical evidence that leveraging DLT, especially hybrid DLT, can eliminate common financial reporting errors in Nigerian banks. The pioneering study expands conceptualizations, theories, and literature regarding DLT's potential to comprehensively transform financial reporting accuracy. It offers important implications for policy, practice, and research on regulating, adopting, and studying DLT solutions to address persistent financial statement errors undermining stakeholder trust in Nigeria's banking sector. The study concludes by strongly recommending for policy and, in practice, the regulation and full adoption of DLT for the elimination of FREs in Nigeria.

Attention improvement to transcranial alternating current stimulation at gamma frequency over the right frontoparietal network: a preliminary report.

Applying transcranial alternating current stimulation (tACS) at 40 Hz to the frontal and parietal regions, either unilaterally (left or right) or bilaterally, can improve cognitive dysfunctions. This study aimed to explore the influence of tACS at gamma frequency over right fronto-parietal (FP) region on attention. The analysis is based on retrospective data from a clinical intervention. We administered test of variables of attention (TOVA; visual mode) to 44 participants with various neuropsychiatric diagnoses before and after 12 sessions of tACS treatment. Alternating currents at 2.0 mA were delivered to the electrode positions F4 and P4, following the 10-20 EEG convention, for 20 mins in each session. We observed significant improvement across 3 indices of the TOVA, including reduction of variability in reaction time (p = 0.0002), increase in d-Prime (separability of targets and non-targets; p = 0.0157), and decrease in commission error rate (p = 0.0116). The mean RT and omission error rate largely remained unchanged. Artificial injection of tACS at 40 Hz over right FP network may improve attention function, especially in the domains of consistency in performance, target/non-target discrimination, and inhibitory control.

The effects of teacher, peer and self-feedback on error correction with corpus use

The strengths of corpora in language learning have been stated, while not many studies have explored the effects of feedback on error correction in the settings of data-driven learning (DDL), which is an approach where learners use corpora to learn language patterns inductively. Therefore, this study examines the effects of feedback on second language (L2) error correction with corpus use. The author hypothesizes that seeing many example sentences of the target word(s) with corpus use is useful in correcting L2 errors and that different sources of feedback have different effects on error correction. To test the hypotheses, the effects of teacher feedback on 55 participants’ error correction with use of the Corpus of Contemporary American English (COCA) were compared with those of peer feedback along with those of self-feedback. The results show that teacher feedback especially worked well for correcting omission errors and agreement errors. The strength of teacher feedback was identifying correctable errors. The results suggest that efficient corpus use for error correction requires teachers to consider appropriate combinations of feedback and error types (e.g., teacher feedback for omission errors and agreement errors).

Estimating the distribution of reedbed in Britain demonstrates challenges of remotely sensing rare land cover types at large spatial scales

Common Reed Phragmites australis, globally one of the mostly widely distributed wetland plants, is important for biodiversity and for humans. However, like most wetland plant communities, reedbed has rarely been mapped at large geographical scales, restricting the information available to study reed’s range dynamics or inform its management. Using Sentinel-2 data and machine learning, we aimed to produce the first published remotely-sensed reedbed map of Britain; however, accuracy as assessed by field validation was relatively low (AUC = 0.671), with many false positives (commission error of 93.4%). A similar workflow carried out in Google Earth Engine, using nearly an order of magnitude more images, gave a lower commission error but a disproportionately higher omission error. Using the known commission and omission error, we estimate that in 2015–2017 ~ 7800 ha of Britain was reedbed. Our study highlights the enduring barriers to accurate land cover classification at large spatial scales. Even with a ‘big data’ approach, reflectance error and ecological factors such as confusion land cover types and geographical variation in temporal reflectance function will probably continue to limit the size of area for which land cover can be classified accurately, therefore limiting the utility of remote sensing for ecologists.

Grammatical Errors in Students’ Recount Text Writing (A Case Study in The Third Grade of SMAN 22 Jakarta)

This study is aimed to find out grammatical errors and the sources of errors in students’ recount texts. The subject of this study is the third-grade students of SMAN 22 Jakarta in the academic year 2023/2024 which consists of 23 students. Specifically, their recount texts focused on recounting their vacation activities. From the entire third-grade class of 23 students, 16 students were selected as the study sample using the purposive sampling technique. This study used a qualitative approach to analyze and identify grammatical errors. The analysis was carried out by referring to Dulay, Burt & Krashen’s theory, a surface strategy taxonomy consisting of Omission, Addition, Misformation, and Misordering and the sources of errors based on Ellis’s theory are Interlingual Transfer and Intralingual Transfer. The result of this study showed that the total number of grammatical errors made by the students in their recount texts is 32 errors. Which consist of 4 omission errors, 7 addition errors, 8 misordering erros, and 13 misinformation errors. Accordingly, the most frequent error is misinformation error which corresponds to 13 (40,6%) of the total error. Furthermore, this study also revealed that most of the errors are caused by Intralingual Transfer (30 errors or 94% of the total error). Based on these findings, it concluded that the students had some difficulties in applying English grammar rules in their writings. Therefore, it is suggested to be aware and pay more attention to improve the English teaching and learning process.

Enhancing burned area monitoring with VIIRS dataset: A case study in Sub-Saharan Africa

Since 2001, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the Aqua and Terra platforms has made great strides in providing information on global burned areas (BA). However, the MODIS mission is nearing its end. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensors, presented as the MODIS Aqua heritage, could be an excellent alternative to ensure the temporal continuity of this information at a moderate resolution. This paper describes and evaluates the effectiveness of our developed hybrid algorithm, which utilizes VIIRS reflectance and active fire products on the Google Earth Engine platform, in producing efficient information about BA. The study investigates the algorithm's performance in sub-Saharan Africa as the region of interest in 2019, using biweekly outputs and a spatial resolution of 250 m. The algorithm encompasses several steps, including pre-processing individual scenes, creating cloud-free composites, generating binary reference data for burned and non-burned areas, conducting a supervised classification using random forest, and performing region shaping. The VIIRS-BA final product, which includes three confidence levels (low, moderate, and high) known as the uncertainty layer, is compared to four other burned area products. The validation is conducted against 27 reference sampling units from the Sentinel-2 Burned Area Reference Database dataset, allowing for a comprehensive uncertainty assessment across five various biomes. The VIIRS-BA product identified 5.1 million km2 of BA, which was significantly larger than other global coarse resolution BA products such as FireCCI51, FireCCIS310, and MCD64A1 and close to the fine resolution FireCCISFD20 with a difference of 7.3%. The differences were less significant in biomes such as “Tropical Savannas” and “Temperate Grasslands” which are characterized by persistent biomass burning. Based on a stratified random sampling, the validation results demonstrate varying levels of accuracy for the VIIRS-BA product across different confidence levels. The commission error (CE) ranges from 7.8% to 23.4%, while the omission error (OE) falls between 29.4% and 58.8%. Notably, there is a significant reduction in OE (ranging from 40.7% to 50.5%) compared to global BA products like FireCCI51, FireCCIS310, and MCD64A1. When compared to VIIRS-BA, the FireCCISFD20 regional product has a 37% better OE performance. While VIIRS-BA shows great potential in detecting fires that global products miss, the VIIRS-BA with low confidence level tends to overestimate BA in regions with high fire activity. To address this, future versions of the algorithm will integrate the updated VIIRS reflectance data alongside VIIRS active fire from the National Oceanic and Atmospheric Administration to reduce CE and improve understanding spatial patterns.

E-Procurement and Performance of Humanitarian United Nation Agencies and Non-Governmental Organizations in South Sudan

Manual procurement processes are expensive in terms of human resource cost, purchasing cost, wastage due to errors of commission or omissions, cause of delay in delivery of goods, services and works which accounts for 40% of losses to inefficiencies, and attracts corruption in the system amongst others. The study was guided by four objectives: To assess the role of e-tendering on performance of humanitarian United Nations agencies and non-governmental organizations in South Sudan. To examine effect of e-contract management on performance of humanitarian United Nations agencies non-governmental organizations in South Sudan. To evaluate effect of e-order processing on performance of humanitarian United Nations agencies non-governmental organizations in South Sudan and to explore influence of e-supplier management on performance of humanitarian United Nations agencies non-governmental organizations in South Sudan. The study methodology adopted explanatory research design of cross-sectional type for the population under study. There were over 235 National Non-governmental organizations, 150 International organizations in addition to over 15 United Nations agencies, bringing the total target population to 400 organizations. This study targeted 400 humanitarian organizations operating in South Sudan. Therefore, the sample size of 200 humanitarian organizations operating in South Sudan were selected using stratified random sampling technique. Data was collected using structured questionnaires which was dropped and picked later or emailed. The data collected from two hundred (200) respondents was coded into statistical package of social sciences (IBM SPSS v29) data analysis tool. The researcher covered a total of 20 respondents for pilot testing. Content validity was also conducted while reliability was conducted using Cronbach alpha. The data was presented in inform of graphs, charts, bars for descriptive analysis. Reliability findings indicated that e-tendering had Cronbach Alpha Coefficient of 0.873, e-contract management had 0.852, e-order processing had 0.872, e-supplier management had 0.736, and performance had 0.859. All the variables had Cronbach Alpha Coefficients greater than 0.7 and this denoted that the questionnaire was reliable. Validity findings reported that KMO value was 0.623 which was above the recommended minimum threshold of 0.5. This statistic implies that the statements in the questionnaire were adequate. In addition, results of the Bartlett roundness test yielded a chi-square statistic of 41.373 and a p-value of 0.000<0.05. These statistics indicate that the statements were correlated and therefore suitable for further analysis. The study found that e-tendering, e-contract management, e-order processing, and e-supplier management have a positive and significant relationship with performance. The study hence concluded that e-tendering, e-contract management, e-order processing, and e-supplier management have a positive and significant effect on performance of humanitarian United Nations agencies non-governmental organizations in South Sudan. Based on these findings, the study suggested that all NGOs and humanitarian agencies should ensure they adopt the use of e-platforms in conducting activities relating to procurement. The study also recommends that the policy makers that make policies relating to humanitarian agencies and NGOs should formulate for them a policy that will enable them make use of the e-platform in conducting procurement activities without any limitations.

Perceptual inference corrects function word errors in reading: Errors that are not noticed do not disrupt eye movements

Both everyday experience and laboratory research demonstrate that readers often fail to notice errors such as an omitted or repeated function word. This phenomenon challenges central tenets of reading and sentence processing models, according to which each word is lexically processed and incrementally integrated into a syntactic representation. One solution would propose that apparent failure to notice such errors reflects post-perceptual inference; the reader does initially perceive the error, but then unconsciously ’corrects’ the perceived string. Such a post-perceptual account predicts that when readers fail to explicitly notice an error, the error will nevertheless disrupt reading, at least fleetingly. We present a large-scale eyetracking experiment investigating whether disruption is detectable in the eye movement record when readers fail to notice an omitted or repeated two-letter function word in naturalistic sentences. Readers failed to notice both omission and repetition errors over 36% of the time. In an analysis that included all trials, both omission and repetition resulted in pronounced eye movement disruption, compared to reading of grammatical control sentences. But in an analysis including only trials on which readers failed to notice the errors, neither type of error disrupted eye movements on any measure. Indeed, there was evidence in some measures that reading was relatively fast on the trials on which errors were missed. It does not appear that when an error is not consciously noticed, it is initially perceived, and then later corrected; rather, linguistic knowledge influences what the reader perceives.

MoMFormer: Mixture of modality transformer model for vegetation extraction under shadow conditions

Accurate estimation of fractional vegetation coverage (FVC) is essential for assessing the ecological environment and acquiring ecological information. However, under natural lighting conditions, shadows in vegetation scenes can easily lead to confusion between shadowed vegetation and shadowed soil, leading to misclassification and omission errors. This issue limits the precision of both vegetation extraction and FVC estimation. To address this challenge, this study introduces a novel deep learning model, the Mixture of Modality Transformer (MoMFormer), which is specifically designed to mitigate shadow interference in vegetation extraction. Our model uses the Swin-transformer V2 as a feature extractor, effectively capturing vegetation features from a dual-modality (regular-exposure RGB and high dynamic range HDR) dataset. A dynamic aggregation module (DAM) is integrated to adaptively blend the most relevant vegetation features. We selected several state-of-the-art (SOTA) methods and conducted extensive experiments using a self-annotated dataset featuring diverse vegetation–soil scenes and compare our model with several state-of-the-art methods. The results demonstrate that MoMFormer achieves an accuracy of 89.43 % on the HDR-RGB dual-modality dataset, with an FVC accuracy of 87.57 %, outperforming other algorithms and demonstrating high vegetation extraction accuracy and adaptability under natural lighting conditions. This research offers new insights into accurate vegetation information extraction in naturally lit environments with shadows, providing robust technical support for high-precision validation of vegetation coverage products and algorithms based on multimodal data. The code and datasets used in this study are publicly available at https://github.com/hhhxiaohe/MoMFormer.

Evaluation of guided reporting: quality and reading time of automatically generated radiology report in breast magnetic resonance imaging using a dedicated software solution.

Unstructured, free-text dictation (FT), the current standard in breast magnetic resonance imaging (MRI) reporting, is considered time-consuming and prone to error. The purpose of this study is to assess the usability and performance of a novel, software-based guided reporting (GR) strategy in breast MRI. Eighty examinations previously evaluated for a clinical indication (e.g., mass and focus/non-mass enhancement) with FT were reevaluated by three specialized radiologists using GR. Each radiologist had a different number of cases (R1, n = 24; R2, n = 20; R3, n = 36). Usability was assessed by subjective feedback, and quality was assessed by comparing the completeness of automatically generated GR reports with that of their FT counterparts. Errors in GR were categorized and analyzed for debugging with a final software version. Combined reading and reporting times and learning curves were analyzed. Usability was rated high by all readers. No non-sense, omission/commission, or translational errors were detected with the GR method. Spelling and grammar errors were observed in 3/80 patient reports (3.8%) with GR (exclusively in the discussion section) and in 36/80 patient reports (45%) with FT. Between FT and GR, 41 patient reports revealed no content differences, 33 revealed minor differences, and 6 revealed major differences that resulted in changes in treatment. The errors in all patient reports with major content differences were categorized as content omission errors caused by improper software operation (n = 2) or by missing content in software v. 0.8 displayable with v. 1.7 (n = 4). The mean combined reading and reporting time was 576 s (standard deviation: 327 s; min: 155 s; max: 1,517 s). The mean times for each reader were 485, 557, and 754 s, and the respective learning curves evaluated by regression models revealed statistically significant slopes (P = 0.002; P = 0.0002; P < 0.0001). Overall times were shorter compared with external references that used FT. The mean combined reading and reporting time of MRI examinations using FT was 1,043 s and decreased by 44.8% with GR. GR allows for complete reporting with minimized error rates and reduced combined reading and reporting times. The streamlining of the process (evidenced by lower reading times) for the readers in this study proves that GR can be learned quickly. Reducing reporting errors leads to fewer therapeutic faults and lawsuits against radiologists. It is known that delays in radiology reporting hinder early treatment and lead to poorer patient outcomes. While the number of scans and images per examination is continuously rising, staff shortages create a bottleneck in radiology departments. The IT-based GR method can be a major boon, improving radiologist efficiency, report quality, and the quality of simultaneously generated data.

A tale of two distrusts: Memory distrust toward commission and omission errors in the Chinese context.

A tale of two distrusts: Memory distrust toward commission and omission errors in the Chinese context.

Differential effects of social versus monetary incentives on inhibitory control under acute inflammation

While the impact of chronic, low-grade inflammation on cognitive functioning is documented in the context of neurodegenerative disease, less is known about the association between acute increases in inflammation and cognitive functioning in daily life. This study investigated how changes in interleukin-6 (IL-6) levels were associated with performance on an inhibitory control task, the go/no-go task. We further examined whether the opportunity to earn different incentive types (social or monetary) and magnitudes (high or low) was associated with differential performance on the task, depending on IL-6 levels. Using a within-participant design, individuals completed an incentivized go/no-go task before and after receiving the annual influenza vaccine. Multilevel logistic regressions were performed on the trial-level data (Nobs = 30,528). For no-go trials, we did not find significant associations between IL and 6 reactivity between the sessions and changes in trial accuracy. For go trials, we found significant differences in the associations between IL and 6 reactivity and changes in accuracy from session 1 to session 2 as a function of the incentive condition. Notably, greater IL-6 reactivity was consistently associated with fewer omission errors (i.e., greater accuracy on go trials) on high-magnitude social incentives (i.e., viewing a picture of a close-other picture) when compared to both low-magnitude social and high-magnitude monetary incentives. Together, these results suggest that mild fluctuations in inflammation might alter the valuation of an incentive, and possibly a shift toward devoting greater attentional resources when a large social incentive is on the line. Overall, this study sheds light on how everyday, low-grade fluctuations in inflammation may influence cognitive abilities essential for daily life and effective inhibitory control.

Medication Errors in Saudi Arabian Hospital Settings: A Systematic Review.

Background and Objectives: Medication errors significantly impact patient safety, potentially causing adverse drug events (ADEs), increasing morbidity and mortality and prolonging hospital stays. This systematic review aimed to identify common medication errors in Saudi hospitals, their contributing factors, and effective prevention strategies. Materials and Methods: Following PRISMA-P guidelines, a comprehensive review of the literature published after 2019 was conducted. Inclusion criteria focused on peer-reviewed articles in English addressing medication errors in Saudi hospitals. Exclusion criteria eliminated reviews, opinion pieces, and non-peer-reviewed sources. A narrative synthesis identified common themes, and a descriptive analysis organized the data. Results: Searches yielded 22 articles from Embase (n = 4), PubMed (n = 10), and Web of Science (n = 8). After removing duplicates and one review article, twelve studies remained. Hand-searching references added 16 more, totaling 28 articles. Of the 28 included studies, 20 (71.4%) reported the types of medication errors observed. Wrong dose and improper dose errors are among the most frequently reported across multiple studies, while prescribing errors remain consistently high, indicating a critical area for intervention. Although less frequent, omission errors still hold significance. Conclusions: This review emphasizes the importance of comprehensive, proactive approaches to preventing medication errors. Integrating evidence-based strategies, fostering a safety culture, and continuously monitoring and evaluating interventions can significantly enhance medication safety and improve patient outcomes in Saudi Arabian hospitals.