Types Of Errors Research Articles

Prevalence of Visual Impairment in Children with Autism Spectrum Disorder: A Mini Review

The association between Visual Impairment (VI) and Autism Spectrum Disorder (ASD) remains an important research topic among children suffering from developmental delays and disabilities. The objective of this mini review was to evaluate previous literature on prevalence and etiology of visual impairment, frequency of visual impairment and types of refractive errors associated with autism in children. Previous researches on ASD found that the visual impairment is one of the major factors in autistic children. Hence measurement of vision screening in autism disorder is necessary for better explanation of the problem. The etiologies of visual impairment in autistic children were associated with refractive error and strabismus. Identification of refractive error in ASD children and providing them suitable corrective lenses may improve their visual functions and help in their daily activities. With these findings in mind, there is an urgent need to investigate further in this area and developed more detailed research. The findings would help optometrist, ophthalmologist in understanding types of visual impairment in autism school children.

Pre-ANDA strategy and human factors activities to de-risk pharmaceutical companies ANDA submission of drug-device combination products: case study of a formative comparative use human factors study.

This article presents a strategy that a Drug Delivery Device Developer (DDDD) has adopted to support Abbreviated New Drug Application (ANDA) submissions of drug-device combination products. As per the related FDA guidance, a threshold analysis should be compiled. If 'other differences' between the Reference Listed Drug (RLD) and the generic drug devices are identified, a Comparative Use Human Factors (CUHF) study may be requested. The DDDD performed task analysis and physical comparison to assess the pen injector design differences. Then, a formative CUHF study with 25 participants simulating injections using both RLD and the generic pen injectors was conducted. After each participant completed four simulated injections, similar type and rates of use error between the RLD (0.70) and generic (0.68) pen injectors were observed. DDDDs can support pharmaceutical companies in the ANDA submission strategy of their drug-device combination product by initiating comparative task analysis and physical comparison of the device as inputs for the threshold analysis. If 'other differences' are identified, a formative CUHF study can be performed. As shown in our case study, this approach can be leveraged to support the sample size calculation and non-inferiority margin determination for a CUHF study with the final combination product.

Effect of in-hospital training on medical errors made by teams during neonatal resuscitation.

Neonatal resuscitation is one of the most critical and risky events that requires a high level of individual skill and team performance. To evaluate the effect of training of resuscitation teams on the frequency and type of medical errors (ME) that result from neonatal resuscitation. A prospective observational study was performed using a checklist to detect ME related to neonatal resuscitation. The rate of ME was 24.82%. There was a significant reduction in the percentage of errors from 17.28% in pre- pre-training phase to 7.54% in post post-training phase. Near miss MEs (98.77%) were significantly higher than adverse events. The active errors were significantly higher than latent errors, P <0.001, and decreased from 39.19% during pre-training to 19.64% in the post-training phase. The commission ME s were significantly higher than the omission, P <0.001. The latent errors percentage was 41.17% of the total errors and were not significantly reduced after training. Training sessions reduced ME that occurred during resuscitation, however careful distinguishing and recognizing the type of MEs is important to plan for further reduction of errors. Special attention to latent errors is imperative as it needs a specific approach rather than just training.

A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction.

Error analysis plays a crucial role in clinical concept extraction, a fundamental subtask within clinical natural language processing (NLP). The process typically involves a manual review of error types, such as contextual and linguistic factors contributing to their occurrence, and the identification of underlying causes to refine the NLP model and improve its performance. Conducting error analysis can be complex, requiring a combination of NLP expertise and domain-specific knowledge. Due to the high heterogeneity of electronic health record (EHR) settings across different institutions, challenges may arise when attempting to standardize and reproduce the error analysis process. This study aims to facilitate a collaborative effort to establish common definitions and taxonomies for capturing diverse error types, fostering community consensus on error analysis for clinical concept extraction tasks. We iteratively developed and evaluated an error taxonomy based on existing literature, standards, real-world data, multisite case evaluations, and community feedback. The finalized taxonomy was released in both .dtd and .owl formats at the Open Health Natural Language Processing Consortium. The taxonomy is compatible with several different open-source annotation tools, including MAE, Brat, and MedTator. The resulting error taxonomy comprises 43 distinct error classes, organized into 6 error dimensions and 4 properties, including model type (symbolic and statistical machine learning), evaluation subject (model and human), evaluation level (patient, document, sentence, and concept), and annotation examples. Internal and external evaluations revealed strong variations in error types across methodological approaches, tasks, and EHR settings. Key points emerged from community feedback, including the need to enhancing clarity, generalizability, and usability of the taxonomy, along with dissemination strategies. The proposed taxonomy can facilitate the acceleration and standardization of the error analysis process in multi-site settings, thus improving the provenance, interpretability, and portability of NLP models. Future researchers could explore the potential direction of developing automated or semi-automated methods to assist in the classification and standardization of error analysis.

Quantum state engineering by steering in the presence of errors

Quantum state engineering plays a vital role in various applications in the field of quantum information. Different strategies, including drive-and-dissipation, adiabatic cooling, and measurement-based steering, have been proposed for state generation and manipulation, each with its upsides and downsides. Here, we address a class of measurement-based state engineering protocols where a sequence of generalized measurements is employed to steer a quantum system toward a desired (pure or mixed) target state. Previously studied measurement-based protocols relied on idealized procedures and avoided exploration of the effects of various errors stemming from imperfections of experimental realizations and external noise. We employ the quantum trajectory formalism to provide a detailed analysis of the robustness of these steering protocols against multiple classes of errors. We study a set of realistic errors that can be classified as dynamic or static, depending on whether they remain unchanged while running the protocol. More specifically, we investigate the impact of the erroneous choice of detector-system coupling, erroneous re-initialization of the detector state following a measurement step, fluctuating steering directions, and environmentally induced errors in the detector-system interaction. We show that the protocol remains fully robust against the erroneous choice of detector-system coupling parameters and presents reasonable robustness against other types of errors. Our analysis employs various quantifiers such as fidelity, trace distance, and linear entropy to characterize the protocol's robustness and provide analytical results for these quantifiers against various errors. We introduce averaging hierarchies of stochastic equations describing individual quantum trajectories associated with detector readouts. Subsequently, we demonstrate the commutation between the classical expectation value and the time-ordering operator of the exponential of a Hamiltonian with multiplicative white noise, as well as the commutation of the expectation value and the partial trace with respect to detector outcomes. Our ideas are implemented and demonstrated for a specific class of steering platforms, addressing a single qubit. Published by the American Physical Society 2024

Is refractive error a factor affecting scoliosis?

Scoliosis is one of the most common surgical disorders of the pediatric spine. Refractive errors are commonly associated with vision impairment worldwide. However, it is currently unclear whether refractive error correlates directly with the development of scoliosis. A cross-sectional study was performed in 2023, and a stratified cluster sampling technique was employed among school-aged students in Nantong City, China. Univariate and multivariate logistic regression analyses were used to investigate specific correlations between scoliosis and related parameters; various types of refractive errors were also included in the study. The prevalence of scoliosis among school-aged students was 2.2% in Nantong city. Multiple logistic regression analyses showed that myopia, hyperopia, astigmatism, and anisometropia were not correlated with the development of scoliosis (all, p≥0.05). Lower body mass index (BMI) [adjusted odds ratio (aOR) = 0.92; 95% confidence interval (CI): 0.88-0.95; p<0.001], living in rural areas (aOR = 1.40; 95% CI: 1.05-1.86; p = 0.020), and older age (aOR = 1.32; 95% CI: 1.25-1.38; p<0.001) had significantly higher risks of scoliosis. Refractive errors did not correlate with the development of scoliosis. However, BMI, living in rural areas and older age did correlate with the development of scoliosis.

Future teachers’ reflections on mathematical errors made in their teaching practice

AbstractThis study answers the following research questions: 1) What types of mathematical errors do future teachers identify when they reflect on their practice? and 2) Which levels of development of the didactic suitability assessment competence for the “errors” component can be inferred when they reflect on their practice? To answer these questions, we explain the Didactic Suitability Criteria construct and describe the associated training cycle structure in the theoretical and methodological framework sections. We followed a qualitative research methodology that mainly consists of thematic analysis. The study conducted allows finding inductive categories of types of mathematical errors, such as error in the task instructions, error of proposition, procedural error, error in the representation, error in the definition and error in the argument. It also enables establishing levels of development of the didactic suitability assessment competence of future teachers for the “errors” component. The main conclusion of this research is the importance of the context to decide what a mathematical error is. The need to further examine the notion of mathematical error in the training of future mathematics teachers is also stressed. Another conclusion is the development of a rubric that allows for more accurate and deeper reflections of future teachers on the errors made.

The Anthropocentric Bias in Animal Cognition

In the classical philosophical tradition, animals had the special function of serving as “objects of comparison” concerning humans. In that sense, philosophy adopted a peculiar comparative perspective focused on the categoric difference that separates humans from other creatures: an exceptionalist perspective. The Humanities developed an anthropocentric canon for the study of animals and privileged the search for differences over similarities of these with humans. On the other hand, the great boost that animal studies received under the influence of Darwin's work promoted a different comparative perspective in the natural sciences. However, especially in comparative psychology, ingent efforts were devoted to avoid the errors that anthropomorphism would entail: attributing human properties to other creatures and privileging similarities over differences. It assumed that anthropomorphic bias entails a more fundamental type of error than anthropocentric bias. Now, this asymmetric diagnosis has beenunmasked with different arguments. In the context of both disciplinary traditions, it is timely to reexamine the most persistent and negative manifestations of anthropocentric bias as a comparative bias for the study of animal cognition. In this work I will identify the following: the homogenization of animals into a single general category; psychological speciesism and the “de-mentalization” of animals; the survival of a hierarchical conception of cognitive abilities; the selective application - only to animals - of Morgan's Canon or anthropodenial and its complement, the assumption of idealized mental capacities in the human case or anthropofabulation; asymmetrical or distorsive methodological strategies for the study of animals versus humans which affects the comparative interpretations; and different manifestations of semantic anthropocentrism.

Quantum control of a cat qubit with bit-flip times exceeding ten seconds.

Quantum bits (qubits) are prone to several types of error as the result of uncontrolled interactions with their environment. Common strategies to correct these errors are based on architectures of qubits involving daunting hardware overheads1. One possible solution is to build qubits that are inherently protected against certain types of error, so the overhead required to correct the remaining errors is greatly reduced2-7. However, this strategy relies on one condition: any quantum manipulations of the qubit must not break the protection that has been so carefully engineered5,8. A type of qubit known as a cat qubit is encoded in the manifold of metastable states of a quantum dynamical system, and thereby acquires continuous and autonomous protection against bit-flips. Here, in a superconducting-circuit experiment, we implemented a cat qubit with bit-flip times exceeding 10 s. This is an improvement of four orders of magnitude over previously published cat-qubit implementations. We prepared and imaged quantum superposition states, and measured phase-flip times greater than 490 ns. Most importantly, we controlled the phase of these quantum superpositions without breaking the bit-flip protection. This experiment demonstrates the compatibility of quantum control and inherent bit-flip protection at an unprecedented level, showing the viability of these dynamical qubits for future quantum technologies.

Error detection for radiotherapy planning validation based on deep learning networks.

Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks. The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations. We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3mm; 3%, 2mm; 2%, 2mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators. The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors. When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

Medication Reconciliation

The Institute of Medicine (IOM) stated that preventable medication errors are the mostcommon type of errors in healthcare. It is of fundamental significance when building a safercare continuum, as it highlights the reason for continuous and more vigilant medicationreconciliation and required effort at all interfaces of care, including community. Without arobust medication reconciliation process, the potential for catastrophic outcomes remains aconstant concern. Prevention of medication errors is essential through strategies that are basedin evidence of medication reconciliation strategies on medication errors in community

Development and implementation of the external quality assessment program for erythrocyte sedimentation rate in hematology laboratories in Thailand

Background: The erythrocyte sedimentation rate (ESR) remains the most widely used screening test for monitoring the course of infections, inflammatory diseases, and some types of cancer. Proficiency Testing (PT) programs are unavailable in Thailand for ESR. Objective: This study aimed to develop and initiate the PT program to improve testing quality through evaluation and analysis of the laboratory test results for ESR. Materials and methods: The PT program for ESR was established at the AMS CMU EQA Unit by the ISO/IEC17043:2010 requirements. The unit produced and sent the ESR control materials to participants for laboratory analysis; the test results were returned to the unit. The PT program was carried out in two cycles per year, with two samples in each cycle. The performance of each laboratory was assessed using robust Z score and performance score. Results: ESR control materials presented with satisfactory homogeneity and stability according to ISO 13528: 2015. One hundred and four laboratories were enrolled in the PT program. Of these laboratories, 96 (92.3%) returned their data: 40 (42.7%) used the modified Westergren method, 29 (29.2%), 14 (15.7%), and 13 (12.4%) used Microsed, Mixrate, and other methods, respectively. Satisfactory and questionable performance was obtained in 82/96 (85.4.%) and 8/96 (8.3%), respectively. Unsatisfactory performance was noted at 6/96 (6.3%). The level of excellent performance was achieved by 82 (approx. 85%) of these laboratories. Two main types of errors found from analyzing the received data were (i) specimen processing and (ii) incorrect identification of the analytical method. Conclusion: Our results indicate that the advantage of participation in the PT program for ESR is that the laboratories can monitor and investigate the source of laboratory errors. Therefore, the PT program for ESR testing should become an integral part of the quality assurance system in the laboratory.

Analyzing Writing Errors on Narrative Texts Made by Grade Nine Students’ of SMP IT Qurrata A’yun Palu

Students often struggle with grammatical accuracy when writing in English as a second language. This study investigates the types and frequencies of errors made by ninth-grade Indonesian students in their English narrative compositions. The research objectives were to 1) identify common error categories within student-generated narrative texts and 2) determine which error type occurs most frequently. The study utilized a descriptive qualitative approach. Ninth-grade students at SMP IT Qurata’Ayun Palu were assigned a narrative writing task on a designated topic. The collected texts were analyzed using Dulay, Burt, and Krashen's (1982) surface approach error taxonomy. Results indicated a total of 70 errors across the students' work. The most prevalent error type was misformation (50%), followed by omission (36%), addition (13%), and misordering (1%). These findings suggest that difficulties applying correct grammatical forms are a significant obstacle to accurate narrative writing for these students.

Adaptive merging migration

Velocity errors and data noise are inevitable for seismic imaging of field data sets in current production; therefore, it is desirable to improve the seismic images as part of the migration process to mitigate the influence of such errors and noise. To address this, we develop a new method of adaptive merging migration (AMM). Our method can produce migrated sections of equal quality to conventional migration methods given a correct velocity model and noise-free data. In addition, it can ameliorate the seismic image quality when applied with erroneous migration velocity models or noisy seismic data. AMM uses an efficient recursive Radon transform to generate multiple p-component images, representing migrated sections associated with different local plane slopes. It then adaptively merges the subsections from those p-component images that are less distorted by velocity errors or noise into the whole image. Such merging is implemented by computing adaptive weights followed by a selective stacking. We use three synthetic velocity models and one field data set to evaluate the AMM performance on isolated Gaussian velocity errors, inaccurate smoothed velocities, velocity errors around high-contrast and short-wavelength interfaces, and noisy seismic data. Numerical tests conducted on synthetic and field data sets validate that AMM can effectively improve the seismic image quality in the presence of different types of velocity errors and data noise.

Patterns of prospective memory errors differ in persons with multiple sclerosis

ABSTRACT Introduction Prospective memory (PM) deficits have been documented in multiple sclerosis (MS). This study aimed to explore the specific types of errors made by persons with MS (PwMS), including differences between PwMS and healthy controls (HC) and PwMS who do and do not have impairments in processing speed and/or verbal learning and memory. Method PwMS (n = 111) and HC (n = 75) completed the Memory for Intentions Test (MIST), an objective measure of PM that has five types of errors that can be coded (PM failure, task substitution, loss of content, loss of time, and random errors). The number and types of PM errors were calculated for the overall MIST and six subscales, which break down performance by types of delay (2-Minute and 15-Minute), cue (Time and Event), and response (Verbal and Action). Impairment was defined as performing < 1.5 SD on either the Symbol Digit Modalities Test (SDMT) or Rey Auditory Verbal Learning Test (RAVLT). Bivariate analyses were used to examine group differences, with post-hoc pairwise comparisons with Bonferroni corrections. Results Nearly 93% of PwMS made at least one PM error, compared to 76% of HC (V = .24, p = .001). The most commonly made PM error by PwMS was loss of content errors (45.0%). PwMS made significantly more task substitution errors (26.4% vs. 7.6%, p < .001) and fewer loss of time errors (9.5% vs. 21.2%, p < .001) than HC. Impaired PwMS made more errors than non-impaired PwMS, specifically PM failures on time-based tasks. Conclusions PM errors are common in PwMS, particularly when there are longer delays and time-based cues. Not only do PwMS make more errors than demographically similar HC, but they exhibit different cognitive process failures.

TURBO CODE RE-ANALYSIS USING DIFFERENT ALGORITHMS

Turbo codes are a novel type of forward error correcting codes that have proved to give a perfor- mance close to the channel capacity as proposed by C. Shannon. The parallel concatenation of two identical recursive convolutional encoders that are spaced apart by an interleaver results in a turbo code encoder. The two cascaded decoding blocks used by the turbo code decoder pass along a priori knowledge generated by each block in turn. The decoding approach has the advantage of working iteratively, allowing for an increase in total performance. This study does a performance analysis of turbo codes. The performance study includes two decoding methods: the soft output Viterbi algo- rithm (SOVA), which uses the log-likelihood ratio to produce soft outputs, and the log maximum a posteriori probability (Log-MAP) algorithm. Both punctured and unpunctured codes are studied to determine the impact of utilizing various decoding algorithms. The performance of the two various decoding techniques is then contrasted in terms of bit error rate. MATLAB tools are utilized to per- form simulations.

Visualization of educational data in a German-language corpus of student texts

Introduction. Digital transformation of education involves the use of digital tools in managing the educational process as a whole and in the learning process in individual disciplines. The specifics of teaching a foreign language require special functionality from digital tools. The purpose of the article is to explore the possibilities of a corpus of student texts in a foreign language for analyzing the results of students’ educational work and forming an adapted learning path within the discipline related to the study of a foreign language. Materials and methods. The material for the study was the Petrozavodsk Annotated Corpus of Texts (PACT), containing written works in German and French written by students from 2019 to 2023. Texts are accompanied by attributive information about the author, writing conditions, evaluation and error corrections. A web application has been developed to work with the corpus. The application includes personal accounts for teachers and students. To analyze the state of the learning process and make subsequent decisions on managing the learning process, the web application contains several tools for visualizing error statistics at different levels of detail with the ability to select texts according to different conditions. Results of the study. As part of the study, patterns were identified that connect the types of errors with the genres of texts, with the severity of the errors, and with the emotional and physiological state of the student. Russian-speaking students studying German as a foreign language make the most mistakes in the choice of lexemes, spelling, punctuation and the place of the verb in a sentence. At the same time, genre differences can be observed to distribute the number of errors by their type. There is an increase in the relative number of gross errors by 1.5 times for senior students compared to the first year. Errors in the choice of lexeme are critical for understanding the content of the text. The severity of the error does not depend on the genre of the text. Conclusion. The results of the study can be used to build corpora of student texts with educational analytics functions in dashboard format. The results obtained can be useful in developing teaching materials for the “Foreign Language” discipline.

ChatGPT for use in technology-enhanced learning in anesthesiology and emergency medicine and potential clinical application of AI language models : Between hype and reality around artificial intelligence in medical use

The utilization of AI language models in education and academia is currently asubject of research, and applications in clinical settings are also being tested. Studies conducted by various research groups have demonstrated that language models can answer questions related to medical board examinations, and there are potential applications of these models in medical education as well. This study aims to investigate the extent to which current version language models prove effective for addressing medical inquiries, their potential utility in medical education, and the challenges that still exist in the functioning of AI language models. The program ChatGPT, based on GPT3.5, had to answer 1025 questions from the second part (M2) of the medical board examination. The study examined whether any errors and what types of errors occurred. Additionally, the language model was asked to generate essays on the learning objectives outlined in the standard curriculum for specialist training in anesthesiology and the supplementary qualification in emergency medicine. These essays were analyzed afterwards and checked for errors and anomalies. The findings indicated that ChatGPT was able to correctly answer the questions with an accuracy rate exceeding 69%, even when the questions included references to visual aids. This represented an improvement in the accuracy of answering board examination questions compared to astudy conducted in March; however, when it came to generating essays ahigh error rate was observed. Considering the current pace of ongoing improvements in AI language models, widespread clinical implementation, especially in emergency departments as well as emergency and intensive care medicine with the assistance of medical trainees, is aplausible scenario. These models can provide insights to support medical professionals in their work, without relying solely on the language model. Although the use of these models in education holds promise, it currently requires asignificant amount of supervision. Due to hallucinations caused by inadequate training environments for the language model, the generated texts might deviate from the current state of scientific knowledge. Direct deployment in patient care settings without permanent physician supervision does not yet appear to be achievable at present.

An Examination of EFL Undergraduates’ Writing Error Types and Frequency in WhatsApp Chat Messaging: Exploring Key Factors

English as a Foreign Language (EFL) undergraduates' writing has consistently attracted the attention of educational stakeholders, notably teachers. Studies have been conducted to address writing challenges in various contexts, including social media platforms. However, rare research is available to examine the specific types and frequency of writing errors committed by EFL undergraduates while using WhatsApp, particularly in the context of Najran University. Therefore, the current study aimed to examine the types of errors and their frequency that EFL students commit in WhatsApp chat messages. Following a descriptive research design, this study used WhatsApp chat messaging samples and conducted semi-structured interviews with a sample of 40 English undergraduates. The content analysis of students' chat messages on WhatsApp indicated errors in spelling, verb tense, capitalization, noun number, and punctuation. Among these, the most frequent error was spelling. Additionally, the analysis of semi-structured interviews identified factors responsible for these errors, including excessive dependence on abbreviations and shortcuts, lack of familiarity with formal and informal communication, inadequate attention to grammar standards, limited exposure and practice with English language writing conventions, unfamiliarity with the formatting choices in WhatsApp, and interruptions from notifications and other conversations. Based on the findings, suggestions and recommendations are presented to address the writing issues of EFL undergraduates.

Image Generation from Text

Abstract: This work diverts from the usual by using Generative Adversarial Networks (GANs) to intentionally create flawed yet captivating images. We introduce a controlled chaos system, where a stalling generator network and a disoriented discriminator network co-exist. This strange training method encourages the development of peculiar irregularities within semi-realistic images. We welcome the unpredictability that arises from this chaos, purposely avoiding issues like mode collapse. Moreover, the project delves into a new evaluation paradigm for evaluating these deliberately flawed images. We go beyond typical metrics, exploring the use of faulty metrics and questionable visual inspection techniques. In addition, we investigate the impact of clumsy dataset selection, potentially employing unconventional or tampered datasets to train the GAN for specific types of errors. This work explores the potential of GANs for jumbled up art creation, data manipulation, and image confusion, opening a new frontier in GAN applications. While potentially causing confusion in the field, this research also reveals the unimaginable artistic and data exploration capabilities of GANs.