Omission Errors Research Articles

P159 Development of digital neuropsychological battery: a use case in Indian SLE patients

Abstract Background/Aims While cognitive dysfunction and mood disorders are frequently reported as adverse effects of SLE, there is a scarcity of neuropsychological (NP) data for Asian SLE patients. Existing English-centric NP tests are inadequate for the linguistic and culturally diverse population. This study examines SLE's effects on mood and cognitive functions using an indigenously developed language-neutral digital NP test battery and validated anxiety, stress, depression and quality of life instruments. Methods We recruited SLE patients fulfilling 2012 SLE classification criteria as cases and caregivers (CG) and college students (CS) as controls aged above 18 years and at least 10th standard education. All participants provided written informed consent. Institutional ethics committees approved the study. The NP battery comprised five cognitive tasks: Montreal Cognitive Assessment (MoCA) and modified versions of attention network test (ANT), sustained attention to response task (SART), picture naming and N-back, and five psychological tests (PHQ-9, GAD-7, STAI-T, PSS-4 and WHOQOL-BREF). Mann-Whitney test with conditional Bonferroni's corrections was used to compare cases with CG and CS. Results Total of 142 volunteers (age: median = 25.0, IQR = 9.0, cases [n = 82], CG [n = 40] and CS [n = 20]) participated in the study. Cases exhibited delayed response latencies in N-back, SART, and picture naming tasks compared to controls. They demonstrated reduced accuracy in all N-back conditions, with significant differences observed in hit rate, miss rate, false alarms, and d-prime scores compared to CS with effect size ranging from 0.298 to 0.736 (Table 1). However, the difference was only significant in the 2-back condition compared to CG. Cases displayed lower accuracy in SART Go trials (higher omission errors) than controls but better inhibition in SART No-Go trials (lower commission errors). No differences were observed in alerting, orienting and executive control functions. Cases showed significantly lower scores in MOCA than controls. Cases reported poorer psychological health through increased anxiety and stress levels and reduced WHO-quality of life scores compared to CG. Conclusion Contrary to earlier research that often relied on isolated cognitive assessments, our pioneering approach introduces a tailored test battery to establish cognitive baselines for Indian SLE patients. Disclosure P. Singhal: None. P. Srivastava: None. L. Rajasekhar: None. N. Samhitha: None.

Using ChatGPT-4 to Create Structured Medical Notes From Audio Recordings of Physician-Patient Encounters: Comparative Study

Background Medical documentation plays a crucial role in clinical practice, facilitating accurate patient management and communication among health care professionals. However, inaccuracies in medical notes can lead to miscommunication and diagnostic errors. Additionally, the demands of documentation contribute to physician burnout. Although intermediaries like medical scribes and speech recognition software have been used to ease this burden, they have limitations in terms of accuracy and addressing provider-specific metrics. The integration of ambient artificial intelligence (AI)–powered solutions offers a promising way to improve documentation while fitting seamlessly into existing workflows. Objective This study aims to assess the accuracy and quality of Subjective, Objective, Assessment, and Plan (SOAP) notes generated by ChatGPT-4, an AI model, using established transcripts of History and Physical Examination as the gold standard. We seek to identify potential errors and evaluate the model’s performance across different categories. Methods We conducted simulated patient-provider encounters representing various ambulatory specialties and transcribed the audio files. Key reportable elements were identified, and ChatGPT-4 was used to generate SOAP notes based on these transcripts. Three versions of each note were created and compared to the gold standard via chart review; errors generated from the comparison were categorized as omissions, incorrect information, or additions. We compared the accuracy of data elements across versions, transcript length, and data categories. Additionally, we assessed note quality using the Physician Documentation Quality Instrument (PDQI) scoring system. Results Although ChatGPT-4 consistently generated SOAP-style notes, there were, on average, 23.6 errors per clinical case, with errors of omission (86%) being the most common, followed by addition errors (10.5%) and inclusion of incorrect facts (3.2%). There was significant variance between replicates of the same case, with only 52.9% of data elements reported correctly across all 3 replicates. The accuracy of data elements varied across cases, with the highest accuracy observed in the “Objective” section. Consequently, the measure of note quality, assessed by PDQI, demonstrated intra- and intercase variance. Finally, the accuracy of ChatGPT-4 was inversely correlated to both the transcript length (P=.05) and the number of scorable data elements (P=.05). Conclusions Our study reveals substantial variability in errors, accuracy, and note quality generated by ChatGPT-4. Errors were not limited to specific sections, and the inconsistency in error types across replicates complicated predictability. Transcript length and data complexity were inversely correlated with note accuracy, raising concerns about the model’s effectiveness in handling complex medical cases. The quality and reliability of clinical notes produced by ChatGPT-4 do not meet the standards required for clinical use. Although AI holds promise in health care, caution should be exercised before widespread adoption. Further research is needed to address accuracy, variability, and potential errors. ChatGPT-4, while valuable in various applications, should not be considered a safe alternative to human-generated clinical documentation at this time.

Analyze of Education and Training Using Ship Engine Simulators at STIP Jakarta Indonesia

On this research paper, we analyze Cadet Engineer, junior engineer, senior level Operational engineers, and senior management level engineers will carry out experiments using speed data from various scenarios on the ship engine simulator and then the experimental results will use educational and training analysis on the ship engine simulator to find out the right time. Later analysis also revealed that human errors occur due to the following reasons: Lack of knowledge of the overall system and fundamental operations, Insufficient confirmation of operations and Incomplete knowledge of the sequence of operations. the role of the instructor will be explained to reduce the causes of human error  and to improve educational performance according to the implementation and development of each scenario based on expert justification and reference to IMO and SOLAS Standards. various safety procedure scenarios for all Engineers in the hope of minimizing accidents that cause loss of life at sea. For the purpose of training engine plant operations using a PC-based Engine Room Simulator (ERS), describe the features of human errors by incompetent trainees in marine engine plant operations and demonstrate the necessary improvements in training to reduce such errors from an educational technology point of view. Examination of these data has shown that errors occur primarily due to errors of omission and inadvertent action. Further analysis was carried out on these errors by the engineer trainees to determine the characteristics of errors caused in the process of acquiring procedural knowledge for operations according to the implemented development of various safety scenarios.

Improved Landsat Operational Land Imager (OLI) Cloud and Shadow Detection with the Learning Attention Network Algorithm (LANA)

Landsat cloud and cloud shadow detection has a long heritage based on the application of empirical spectral tests to single image pixels, including the Landsat product Fmask algorithm, which uses spectral tests applied to optical and thermal bands to detect clouds and uses the sun-sensor-cloud geometry to detect shadows. Since the Fmask was developed, convolutional neural network (CNN) algorithms, and in particular U-Net algorithms (a type of CNN with a U-shaped network structure), have been developed and are applied to pixels in square patches to take advantage of both spatial and spectral information. The purpose of this study was to develop and assess a new U-Net algorithm that classifies Landsat 8/9 Operational Land Imager (OLI) pixels with higher accuracy than the Fmask algorithm. The algorithm, termed the Learning Attention Network Algorithm (LANA), is a form of U-Net but with an additional attention mechanism (a type of network structure) that, unlike conventional U-Net, uses more spatial pixel information across each image patch. The LANA was trained using 16,861 512 × 512 30 m pixel annotated Landsat 8 OLI patches extracted from 27 images and 69 image subsets that are publicly available and have been used by others for cloud mask algorithm development and assessment. The annotated data were manually refined to improve the annotation and were supplemented with another four annotated images selected to include clear, completely cloudy, and developed land images. The LANA classifies image pixels as either clear, thin cloud, cloud, or cloud shadow. To evaluate the classification accuracy, five annotated Landsat 8 OLI images (composed of >205 million 30 m pixels) were classified, and the results compared with the Fmask and a publicly available U-Net model (U-Net Wieland). The LANA had a 78% overall classification accuracy considering cloud, thin cloud, cloud shadow, and clear classes. As the LANA, Fmask, and U-Net Wieland algorithms have different class legends, their classification results were harmonized to the same three common classes: cloud, cloud shadow, and clear. Considering these three classes, the LANA had the highest (89%) overall accuracy, followed by Fmask (86%), and then U-Net Wieland (85%). The LANA had the highest F1-scores for cloud (0.92), cloud shadow (0.57), and clear (0.89), and the other two algorithms had lower F1-scores, particularly for cloud (Fmask 0.90, U-Net Wieland 0.88) and cloud shadow (Fmask 0.45, U-Net Wieland 0.52). In addition, a time-series evaluation was undertaken to examine the prevalence of undetected clouds and cloud shadows (i.e., omission errors). The band-specific temporal smoothness index (TSIλ) was applied to a year of Landsat 8 OLI surface reflectance observations after discarding pixel observations labelled as cloud or cloud shadow. This was undertaken independently at each gridded pixel location in four 5000 × 5000 30 m pixel Landsat analysis-ready data (ARD) tiles. The TSIλ results broadly reflected the classification accuracy results and indicated that the LANA had the smallest cloud and cloud shadow omission errors, whereas the Fmask had the greatest cloud omission error and the second greatest cloud shadow omission error. Detailed visual examination, true color image examples and classification results are included and confirm these findings. The TSIλ results also highlight the need for algorithm developers to undertake product quality assessment in addition to accuracy assessment. The LANA model, training and evaluation data, and application codes are publicly available for other researchers.

Analysis of the hyperparameter optimisation of four machine learning satellite imagery classification methods

AbstractThe classification of land use and land cover (LULC) from remotely sensed imagery in semi-arid Mediterranean areas is a challenging task due to the fragmentation of the landscape and the diversity of spatial patterns. Recently, the use of deep learning (DL) for image analysis has increased compared to commonly used machine learning (ML) methods. This paper compares the performance of four algorithms, Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Convolutional Network (CNN), using multi-source data, applying an exhaustive optimisation process of the hyperparameters. The usual approach in the optimisation process of a LULC classification model is to keep the best model in terms of accuracy without analysing the rest of the results. In this study, we have analysed such results, discovering noteworthy patterns in a space defined by the mean and standard deviation of the validation accuracy estimated in a 10-fold cross validation (CV). The point distributions in such a space do not appear to be completely random, but show clusters of points that facilitate the discovery of hyperparameter values that tend to increase the mean accuracy and decrease its standard deviation. RF is not the most accurate model, but it is the less sensitive to changes in hyperparameters. Neural Networks, tend to increase commission and omission errors of the less represented classes because their optimisation lead the model to learn better the most frequent classes. On the other hand, RF and MLP prediction layers are the most accurate from a general qualitative point of view.

Medication management error types: Associations with mild cognitive impairment subtype

Objective: Medication management errors are suspected to be prevalent among older adults with mild cognitive impairment (MCI). This study examined types of simulated medication-taking errors in cognitively normal older adults (CN; n = 131), single domain amnestic MCI (sdMCI, n = 91), and multi-domain MCI (mdMCI, n = 44). Errors were measured using the medication management ability assessment (MMAA). Methods: 266 participants seen for neuropsychological evaluation (94.4% White, 57.9% female, average age = 72, average education = 14 years) completed the MMAA (version 4.1), a performance-based task of medication management. Group differences in MMAA total scores, accuracy, and error types were evaluated using Kruskall-Wallis H tests. This study was the first to explore a newly operationalized error, perseverations, caused by taking a specific dose ≥2 times during the simulation. Results: CN and sdMCI groups had higher MMAA total scores than individuals with mdMCI, indicating better overall performance. The mdMCI group made a higher number of omission errors (missed pills) than other groups, but no differences were found for commission errors (extra pills). The sdMCI group made more perseverative errors compared to the CN group. Conclusions: Individuals with mdMCI made more simulated medication management errors than CN and sdMCI groups, indicating that they may be most vulnerable to difficulties in medication management. In contrast, sdMCI individuals were more likely to make perseverative errors, which may reflect a tendency towards overcompensation of memory loss. Future studies should assess whether MMAA performance is associated with patterns of real-world medication-taking in more diverse samples of older adults.

Addressing Challenges in Narrative Writing: An Error Analysis of Eleventh Grade Senior High School Students' Use of Simple Past Tense

The motivation behind this review is to distinguish errors in the utilization of the basic past tense by 11th grade at Madrasah Aliyah Nurul Jihad in Indragiri Hilir. The exploration technique is descriptive qualitative, with an emphasis on information got from the writing tests. The information showed that students made errors in utilizing the straightforward past tense, which were delegated omission, addition, misformation, and misordering. There were 104 errors found, including 43 omissions, 16 additions, 20 misformations, and 25 misorderings. The most prevalent type of error was omission (42%), followed by misordering (24%), misformation (19%), and addition (15%). The prevalence of omission errors suggests that students struggle with understanding and applying the simple past tense in their narrative writing, often resulting in incomplete sentences or omitted words. To improve, students should engage in more practice with narrative text writing to enhance their grasp of the simple past tense.

The Effect of Rocking Bed on Mood and Attention

Objective: The current study aims to explore the effects of sleep in the rocking bed on mood and attention.Methods: Thirty-nine young adults (14 with insomnia and 25 without insomnia) slept in the Sway Bed under three different conditions; 1) motionless condition, 2) continuously rocking condition, and 3) before-sleep rocking condition. In the motionless condition, participants slept without rocking for all night. In the continuously rocking condition, rocking started when lights out and stopped at wake-up time. In the before-sleep rocking condition, rocking started when lights out, but stopped after sleep onset. The Profile of Mood States and the computerized Comprehensive Attention Test were conducted before and after experimental sleep.Results: Under continuously rocking, depression, tension, anger, fatigue, confusion, and standard deviation of reaction time in divided attention were reduced. Under after-sleep bed condition, depression, tension, fatigue, confusion and omission errors in divided attention were reduced. There were no significant differences in after-sleep mood or attention changes between bed conditions. However, within those with insomnia, after-sleep improvement of depression and divided reduction were larger under continuously rocking conditions than under motionless conditions.Conclusion: Sleeping in the rocking bed improved negative affects and divided attention. The effects of rocking bed on depression and divided attention were prominent especially in those with insomnia. The current findings suggest that the sleep in the rocking bed may improve the depressive mood and the impaired attention of insomnia patients.

Surface Strategy Taxonomy in Errors Analysis: Made by the Students of Senior High School

This research was conducted to find out and analyze the grammatical errors in translating Indonesian procedure text into English. The aim of this research was to find out and analyze the errors and their types. This problem was categorized under grammatical errors using four types of Dullay’s Strategy Taxonomy, they are omission, addition, misformation, and misordering. The researcher used descriptive qualitative method. Generally, there are several techniques to collect data needed such as the researcher prepared two Indonesian procedure texts, then gave the texts to the students. The researcher used test technique to collect the data needed and the test was done for 60 minutes in class.  The data were presented in the form of table and ordered based on each of the translation problems to make it easier for the analysis. The result of the data analysis reveals that the problems which occurred in the students’ worksheet are related to the types of grammatical errors (33,48 %) omission errors, (4,21 %)  addition errors, (4,44%) misformation errors, (57,84%) misordering errors. The most identified error belongs to misordering error which is given the highest score to the total number of errors identified (57,84%) and the least identified one belongs to addition error category (4,21%). The fact indicates that there are some students who have adequate knowledge in translating the texts. In other words, there are also students who have problems in translating Indonesian procedure texts into English

AN ERROR ANALYSIS ON THE USAGE OF COHESIVE DEVICES IN THE ENGLISH WRITTEN COMPOSITIONS OF ALTERNATIVE LEARNING SYSTEM (ALS) LEARNERS

This qualitative research aimed to identify errors in the usage of cohesive devices in the English written compositions of Alternative Learning System (ALS) learners. Employing Corder's (1974) Error Analysis framework, the study identified error types and frequencies in compositions collected from three Tagum City secondary schools offering ALS programs. A comprehensive error analysis of the ALS learners' English written compositions revealed 126 errors concerning the usage of cohesive devices. The study finds selection errors (48 instances) most prevalent, indicating misuse of cohesive devices that do not accurately convey intended meaning or context. Learners also commit omission errors (30 instances), failing to use cohesive devices that are vital for linking ideas, and addition errors (26 instances), employing unnecessary or incorrect devices that disrupt sentence structure. Less frequent ordering errors (14 instances) involve misplaced devices leading to misinterpretations. These findings highlight the difficulty ALS learners face in using cohesive devices effectively to connect ideas within intended meaning and grammatical context of their written compositions. They underscore the critical need for targeted instructional strategies to address these challenges, aiming to enhance learners' knowledge and skills in utilizing cohesive devices. By mastering their use and application in writing, learners can improve the coherence, cohesion, and overall competence of their written English, ultimately enhancing their writing skills and language proficiency.

Sentinel-2 Reference Fire Perimeters for the Assessment of Burned Area Products over Latin America and the Caribbean for the Year 2019

The increasing availability of products generating burned area (BA) maps in recent years necessitates the creation of more accurate reference perimeters to validate these products and provide users with information about their accuracy. For this purpose, reference perimeters were created using Sentinel-2 images in Latin America and the Caribbean (LAC) for the year 2019. The sampling was adapted to the peculiarities of the Sentinel-2 tiling grid system, and statistically representative sample units were selected for biomes and fire activity through stratified random sampling. Fire perimeters were extracted using a Random Forest supervised classification and results were manually supervised and refined. Efforts were made to maximize the temporal length covered by the reference perimeters for each sample, aiming to minimize temporal errors when using the perimeters for validation. The dataset covers 569,214.2 km2 (3.5% burned, 88.7% unburned, and 7.8% unobserved). These perimeters were compared with higher spatial resolution PlanetScope-derived perimeters, resulting in 8.4% commission errors and 3.8% omission errors. As a validation exercise, MCD64A1 and FireCCI51 global burned area products were validated using the Sentinel-2 reference dataset created, confirming that the temporal extent of the reference perimeters significantly affects the validation of such products. The reference fire perimeters are publicly available in the Burned Area Reference Database (BARD).

A tuning-free moderate-scale burned area detection algorithm — a case study in chornobyl-contaminated region

ABSTRACT The predefined threshold is one of the main fundamental and methodological challenges underlying the uncertainty and delayed release of existing burned area (BA) products. To improve the accuracy and timeliness of BA mapping, a tuning-free moderate-scale BA detection algorithm (AT&RF algorithm) was proposed to carry out rapid BA mapping for the immediate post-fire assessment. A case study was carried out at the Chornobyl Exclusion Zone (ChEZ) to assess the performance of the algorithm. The evaluation results indicated that the algorithm successfully detected the BA in 2015 and 2020 in ChEZ, which outperforms the existing BA products by significantly decreasing the omission error by 18.6% and 15.1% and improving the Kappa coefficient and critical success index by 9.4% and 4.0% on average for MCD64A1 and FireCCI51 products, respectively. It is also possible to transfer the algorithm quickly to other regions without complicated adjustments and significantly improve the timeliness and accuracy compared to the existing global BA products for assessing environmental impact and scope.

Nitrogen dioxide exposure, attentional function, and working memory in children from 4 to 8 years: Periods of susceptibility from pregnancy to childhood

BackgroundAir pollution exposure during pregnancy and childhood has been linked to executive function impairment in children, however, very few studies have assessed these two exposure periods jointly to identify susceptible periods of exposure. We sought to identify potential periods of susceptibility of nitrogen dioxide (NO2) exposure from conception to childhood on attentional function and working memory in school-aged children. MethodsWithin the Spanish INMA Project, we estimated residential daily NO2 exposures during pregnancy and up to 6 years of childhood using land use regression models (n = 1,703). We assessed attentional function at 4–6 years and 6–8 years, using the Conners Kiddie Continuous Performance Test and the Attention Network Test, respectively, and working memory at 6–8 years, using the N-back task. We used distributed lag non-linear models to assess the periods of susceptibility of each outcome, adjusting for potential confounders and correcting for multiple testing. We also stratified all models by sex. ResultsHigher exposure to NO2 between 1.3 and 1.6 years of age was associated with higher hit reaction time standard error (HRT-SE) (0.14 ms (95 % CI 0.05; 0.22) per 10 μg/m3 increase in NO2) and between 1.5 and 2.2 years of age with more omission errors (1.02 (95 % CI 1.01; 1.03) of the attentional function test at 4–6 years. Higher exposure to NO2 between 0.3 and 2.2 years was associated with higher HRT-SE (10.61 ms (95 % CI 3.46; 17.75) at 6–8 years only in boys. We found no associations between exposure to NO2 and working memory at 6–8 years. ConclusionOur findings suggest that NO2 exposure during the first two years of life is associated with poorer attentional function in children from 4 to 8 years of age, especially in boys. These findings highlight the importance of exploring long-term effects of traffic-related air pollution exposure in older age groups.

Understanding the operators’ cloud change errors based on cognitive abilities and personality traits: An investigation integrated with quantitative and qualitative methods

While cloud services make industrial data convenient, they also expose it to cloud incidents. Operators' error in cloud change activities is a leading factor for cloud incidents, which have received relatively less attention in cloud security research. This study conducted a two-stage research process using an integrated approach to explore the stable individual factors related to cloud change errors. First, in the qualitative research, content analysis based on interviews and historical documents was conducted to extract the operator's cognitive abilities and personality traits and develop hypotheses. Five cognitive abilities and six personality traits were extracted. Second, quantitative research based on an experiment was conducted to test relationships between operators' different types of cloud change errors and 1) cognitive ability and 2) personality traits, respectively. Results of error type comparisons suggested that operators generated more uncorrected errors than corrected errors and more operational errors than omission errors in cloud change activities. The multivariate Poisson regression analysis suggested that cognitive abilities of sustained attention, divided attention, and long-term memory negatively predicted the number of operators' total errors, uncorrected errors, and operational errors. Regarding personality traits, with the increase in resilience capacity and carefulness and the decrease in self-esteem, the number of different types of errors reduced, except for omission errors. Working memory and risk-taking propensity were also significant predictors of the number of uncorrected errors with negative and positive coefficients, respectively. Logical reasoning, emotional stability, and sense of responsibility were not observed as predictors of cloud change errors. The present findings have several implications for the industry and cloud providers to enhance industrial cloud data security regarding human cognitive abilities and personality traits.

The Amazon’s 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction

The Amazon, the world’s largest rainforest, faces a severe historic drought. The Rio Negro River, one of the major Amazon River tributaries, reached its lowest level in a century in October 2023. Here, we used a U-net deep learning model to map water surfaces in the Rio Negro River basin every 12 days in 2022 and 2023 using 10 m spatial resolution Sentinel-1 satellite radar images. The accuracy of the water surface model was high, with an F1-score of 0.93. A 12-day mosaic time series of the water surface was generated from the Sentinel-1 prediction. The water surface mask demonstrated relatively consistent agreement with the global surface water (GSW) product from the Joint Research Centre (F1-score: 0.708) and with the Brazilian MapBiomas Water initiative (F1-score: 0.686). The main errors of the map were omission errors in flooded woodland, in flooded shrub, and because of clouds. Rio Negro water surfaces reached their lowest level around the 25th of November 2023 and were reduced to 68.1% (9559.9 km2) of the maximum water surfaces observed in the period 2022–2023 (14,036.3 km2). Synthetic aperture radar (SAR) data, in conjunction with deep learning techniques, can significantly improve near-real-time mapping of water surfaces in tropical regions.

Error Analysis on Students’ Descriptive Writing

Nur Hidayati, 2023. Error Analysis on Students’ Descriptive Writing at Madrasah Aliyah PMU Sanrobone Takalar Regency. Guided by Syamsiarna Nappu and Erwin Akib. This research focused on students’ errors in writing descriptive text. This study aimed to know the types of error were made by eleventh-grade students at Madrasah Aliyah PMU Sanrobone Takalar Regency academic year 2023/2024 in writing descriptive text and to find out the most students’ dominant error in writing descriptive text. This research was conducted using descriptive quantitative study with case study design as quantitative data used. The population of this research is 18 students. Then the data of the research were collected by using test about writing descriptive text. Based on the data analysis result, students made four types of errors based on surface strategy taxonomy. Those were omission, addition, mis-formation, mis-ordering. Students’ highest error frequency is mis-formation error with 219 total number of errors or (36%) total of errors. Omission error with 112 or (19%) total of errors, addition error with 190 or (32%) total of errors. The lowest frequency of error made by the students is mis-ordering error with 80 or (13%) total of errors.

Neurocognitive Functions After 6-Month Buprenorphine (Naloxone)-Based Opioid Agonist Maintenance Treatment: A Controlled Prospective Study.

Medications for opioid use disorder (OUD) may influence neurocognitive functions. Inadequate power, confounders, and practice effects limit the validity of the existing research. We examined the change in cognitive functions in patients with OUD at 6-month buprenorphine (naloxone) posttreatment and compared the cognitive performance of the buprenorphine-treated group with control subjects. We recruited 498 patients with OUD within a week of initiating buprenorphine. Assessments were done twice-at baseline and 6 months. Those abstinent from illicit opioids and adherent to treatment (n = 199) underwent follow-up assessments. Ninety-eight non-substance-using control subjects were recruited from the community. The neurocognitive assessments comprised the Wisconsin Card Sorting Test, Iowa Gambling Task, Trail-Making Tests A and B (TMT-A and TMT-B), and verbal and visual N-Back Test. We controlled for potential effect modifiers. Twenty-five of the 32 test parameters significantly improved with 6 months of buprenorphine treatment; 20 parameters withstood corrections for multiple comparisons (P < 0.001). The improved test domains spread across cognitive tests: Wisconsin Card Sorting Test (perseverative errors and response, categories completed, conceptual responses), TMTs (time to complete), verbal and visual N-Back Tests (hits, omission, and total errors). After treatment, OUD (vs control subjects) had less perseverative response and error (P < 0.001) and higher conceptual response (P = 0.004) and took lesser time to complete TMT-A (P < 0.001) and TMT-B (P = 0.005). The baseline neurocognitive functions did not differ between those who retained and those who discontinued the treatment. Cognitive functions improve in patients with OUD on buprenorphine. This improvement is unlikely to be accounted for by the practice effect, selective attrition, and potential confounders.

Students Error Analysis in Descriptive Text of UNCP

This research deals with the the kinds of errors of the first semester students of Elementary School Teacher Education Study Program of Palopo Cokroaminoto University mostly do in their writing. The population of this research is the first semester students of Elementary School Teacher Education Study Program of Palopo Cokroaminoto University in academic year 2023. The total number of population was 200 students. The researcher applied the purposive sampling technique and there are 20 students as sample. Writing test is the instrument of this research. Based on the findings and discussion in the previous chapter, the researcher concludes the kinds of errors of the first semester students of Elementary School Teacher Education Study Program of Palopo Cokroaminoto University mostly do in their writing is selection. It can be proven by the kinds of error made by the students are selection error (21 errors or 53.84%), omission error (9 errors or 23.07%), and addition error (9 errors or 23.07%).

Where’s the BIPOC Blueprint for Healthy Youth Development? The Role of Scientific Omissions in Our Struggle for Science Translation and Racial Equity in the United States

Prevention Science seeks to advance the prevention research and to translate scientific advances into the promotion of healthy development for all youth. Despite tremendous progress creating a robust evidence-base and set of translational tools, elaborations and expansions for equity are required. Our collective errors of omission as prevention researchers have left prevention practitioners and policy-makers without sufficient information to identify strategies that have been demonstrated to prevent behavioral health problems in young people who identify as Black, Indigenous, or other People of Color (BIPOC). We first describe the current shortcomings of available evidence, and then we call for individual and collective action to conceptualize equity-enhancing prevention, sample more inclusively, and improve analytic approaches such that we can truly promote the healthy development of all youth.

The Spatially Adaptable Filter for Error Reduction (SAFER) Process: Remote Sensing-Based LANDFIRE Disturbance Mapping Updates

LANDFIRE (LF) has been producing periodic spatially explicit vegetation change maps (i.e., LF disturbance products) across the entire United States since 1999 at a 30 m spatial resolution. These disturbance products include data products produced by various fire programs, field-mapped vegetation and fuel treatment activity (i.e., events) submissions from various agencies, and disturbances detected by the U.S. Geological Survey Earth Resources Observation and Science (EROS)-based Remote Sensing of Landscape Change (RSLC) process. The RSLC process applies a bi-temporal change detection algorithm to Landsat satellite-based seasonal composites to generate the interim disturbances that are subsequently reviewed by analysts to reduce omission and commission errors before ingestion them into LF’s disturbance products. The latency of the disturbance product is contingent on timely data availability and analyst review. This work describes the development and integration of the Spatially Adaptable Filter for Error Reduction (SAFER) process and other error and latency reduction improvements to the RSLC process. SAFER is a random forest-based supervised classifier and uses predictor variables that are derived from multiple years of pre- and post-disturbance Landsat band observations. Predictor variables include reflectance, indices, and spatial contextual information. Spatial contextual information that is unique to each contiguous disturbance region is parameterized as Z scores using differential observations of the disturbed regions with its undisturbed neighbors. The SAFER process was prototyped for inclusion in the RSLC process over five regions within the conterminous United States (CONUS) and regional model performance, evaluated using 2016 data. Results show that the inclusion of the SAFER process increased the accuracies of the interim disturbance detections and thus has potential to reduce the time needed for analyst review. LF does not track the time taken by each analyst for each tile, and hence, the relative effort saved was parameterized as the percentage of 30 m pixels that are correctly classified in the SAFER outputs to the total number of pixels that are incorrectly classified in the interim disturbance and are presented. The SAFER prototype outputs showed that the relative analysts’ effort saved could be over 95%. The regional model performance evaluation showed that SAFER’s performance depended on the nature of disturbances and availability of cloud-free images relative to the time of disturbances. The accuracy estimates for CONUS were inferred by comparing the 2017 SAFER outputs to the 2017 analyst-reviewed data. As expected, the SAFER outputs had higher accuracies compared to the interim disturbances, and CONUS-wide relative effort saved was over 92%. The regional variation in the accuracies and effort saved are discussed in relation to the vegetation and disturbance type in each region. SAFER is now operationally integrated into the RSLC process, and LANDFIRE is well poised for annual updates, contingent on the availability of data.