Error Monitoring Research Articles

Quick balance skill improvement after short-term training with error amplification feedback for older adults

This study investigated behavioral and cortical mechanisms for short-term postural training with error amplification (EA) feedback in the elderly. Thirty-six elderly subjects (65.7 ± 2.2 years) were grouped (control and EA, n = 18) for training in stabilometer balance under visual guidance. During the training session (8 training rounds of 60 s in Day 2), the EA group received visual feedback that magnified errors to twice the real size, whereas the control group received visual feedback that displayed real errors. Scalp EEG and kinematic data of the stabilometer plate and ankle joint were recorded in the pre-test (Day 1) and post-test (Day 3). The EA group (−46.5 ± 4.7%) exhibited greater post-training error reduction than that of the control group (−27.1 ± 4.0%)(p = 0.020), together with a greater decline in kinematic coupling between the stabilometer plate and ankle joint (EA: −26.6 ± 4.8%, control: 2.3 ± 8.6%, p = 0.023). In contrast to the control group, the EA group manifested greater reductions in mean phase-lag index (PLI) connectivity in the theta (4–7 Hz)(p = 0.011) and alpha (8–12 Hz) (p = 0.027) bands. Only the EA group showed post-training declines in the mean PLI in the theta and alpha bands. Minimal spanning tree analysis revealed that EA-based training led to increases in the diameter (p = 0.002) and average eccentricity (p = 0.004) of the theta band for enhanced performance monitoring and reduction in the leaf fraction (p = 0.030) of the alpha band for postural response with enhanced automaticity. In conclusion, short-term EA training optimizes balance skill, favoring multi-segment coordination for the elderly, which is linked to more sophisticated error monitoring with less attentive control over the stabilometer stance.

Error-related brain activity in pediatric major depressive disorder: An ERP and time-frequency investigation

BackgroundThe error-related negativity (ERN) reflects individual differences in error monitoring. However, findings on the ERN in adult and adolescent depression have been inconsistent. Analyzing electroencephalographic (EEG) data in both the time- and time-frequency domain can be useful to better quantify neural response to errors. The present study aimed at examining electrocortical measures of error monitoring in early adolescents with and without depression. MethodEEG activity was collected during an arrowhead version of the flanker task in 29 (25 females) early adolescents with depression and 34 without MDD (29 females). ResultsThe depression group showed reduced ERN amplitude, reduced error-related theta power and increased error-related beta power compared to the control group. When all variables that related to MDD diagnosis were considered simultaneously, both theta and beta power, but not the ERN, were independently related to an increased likelihood of being diagnosed with depression. ConclusionsBy examining both time-domain and separate time-frequency measures, the present study provided novel evidence on error monitoring alterations in youth depression, suggesting that depression during adolescence may be characterized by reduced error monitoring (i.e., reduced ERN and error-related theta) and post-error inhibition (i.e., greater error-related beta power). These results support that time-frequency measures might be better suited for examining error-related neural activity in MDD relative to time-domain measures.

RISK-BASED QUALITY CONTROL IN THE SURGICAL PATHOLOGY LABORATORY; BRIEF REVIEW AND DEVELOPMENT OF AN INDIVIDUALIZED QUALITY CONTROL PLAN

One of the most important tools for reducing or preventing diagnostic errors in the surgical pathology laboratory is the establishment of a quality management system. Risk analysis and quality assurance are important components to ensure the sustainability of the quality management system. Quality assurance ensures the integrity of program design and includes interrelated activities quality control and quality monitoring-evaluation programs. Risk-based thinking is expressed as the planning of risks and how they should be managed by determining the possible dangers and risks in achieving the determined goals and objectives, using the available information systematically. Laboratories must identify and carry out a large number of interrelated activities to maintain their functions effectively. To achieve its goals, it must demonstrate the ability to measure and evaluate process performance, as well as to demonstrate the effectiveness of past decisions, along with improving operational effectiveness and efficiency. With a successful quality management process, the reliability of laboratory results can be increased. In this study, our objective was to develop a roadmap for the detection, monitoring and minimization of diagnostic errors in the surgical pathology laboratory, in line with the views of international regulatory organizations, to develop a quality plan based on risk management that covers the entire analytical cycle. It is not possible to discuss the quality control of laboratories without accepting the possibility of error in surgical pathology. Investing in continuing medical education and patient safety, as well as the training of new pathologists with a critical view to reducing errors is an imperative way to improve the practice of pathology.

Modeling and Compensation for Repositioning Error in Discontinuous GBSAR Monitoring

Modeling and Compensation for Repositioning Error in Discontinuous GBSAR Monitoring

Meditators Probably Show Increased Behaviour-Monitoring Related Neural Activity

ObjectivesMindfulness meditation is associated with better attention function. Performance monitoring and error-processing are important aspects of attention. We investigated whether experienced meditators showed different neural activity related to performance monitoring and error-processing. Previous research has produced inconsistent results. This study used more rigorous analyses and a larger sample to resolve the inconsistencies.MethodWe used electroencephalography (EEG) to measure the error-related negativity (ERN) and error positivity (Pe) following correct and incorrect responses to a Go/Nogo task from 27 experienced meditators and 27 non-meditators.ResultsNo differences were found in the ERN (all p > 0.05). Meditators showed larger global field potentials (GFP) in the Pe after correct responses and errors, indicating stronger neural responses (p = 0.019, FDR-p = 0.152, np2 = 0.095, BFincl = 2.691). This effect did not pass multiple comparison controls. However, single-electrode analysis of the Pe did pass multiple comparison controls (p = 0.002, FDR-p = 0.016, np2 = 0.133, BFincl = 220.659). Meditators also showed a significantly larger Pe GFP for errors, which would have passed multiple comparison controls, but was not a primary analysis (p = 0.003, np2 = 0.149, BF10 = 9.999).ConclusionsMeditation may strengthen neural responses related to performance monitoring. However, these strengthened neural responses were not specific to error monitoring (although the error-related Pe may be more sensitive to group differences than the correct response Pe). These conclusions remain tentative, because the single-electrode analysis passed multiple comparison controls, but the analysis including all electrodes did not.PreregistrationThis study was not preregistered.

Internal Echo Chamber: Schizophrenia Source Monitoring Errors, Self-Speech, and Auditory Hallucinations

Internal Echo Chamber: Schizophrenia Source Monitoring Errors, Self-Speech, and Auditory Hallucinations

Humans can monitor trial-based but not global timing errors: Evidence for relative judgements in temporal error monitoring.

Humans can monitor the magnitude and direction of their temporal errors in individual trials. Based on the predictions of our model of temporal error monitoring that rely on a relative comparison of internal clock readings, we predict that participants would monitor their timing errors in individual trials, but not the direction of their global timing errors without external feedback. One study has indeed found that accurate self-monitoring of average timing biases required external feedback with directional information. The current study investigates how different sources of feedback (i.e., internal or external) affect performance in the self-monitoring of average timing bias. Four groups of participants were tested in a temporal reproduction task. Participants in the self-evaluation condition evaluated the direction and size of their time reproduction errors in individual trials. In the accurate feedback condition, participants received explicit trial-based feedback regarding the direction of their error while participants in the partially accurate feedback condition received trial-based feedback according to the accuracy of short-long judgements of another participant in the self-evaluation condition. Participants in the control condition reproduced only the target duration without making any judgements regarding their reproduction performance or receiving any external feedback about it. Results showed that while participants accurately monitor timing errors in individual trials, in none of the experimental conditions were they more accurate than the chance level in terms of evaluating the direction of their average temporal bias. We discuss these results in terms of the temporal error monitoring model introduced by Akdoğan and Balcı. Thus, our findings suggest that external directional feedback does not have any informational value for global temporal bias judgements above and beyond internal self-monitoring.

The gray matter morphology associated with the electrophysiological response to errors in individuals with high trait anxiety

Enhanced error monitoring has been associated with higher levels of anxiety. This has been consistently demonstrated in its most reliable electrophysiological index, the error-related negativity (ERN), such that increased ERN is related with elevated anxiety symptomology. However, it is still unclear whether the structural properties of the brain are associated with individual differences in ERN amplitude. Moreover, the relationship between ERN and anxiety has recently been suggested to be moderated by sex, but the degree to which sex moderates the association between brain structure and ERN amplitude is unknown. The present study investigated the association between gray matter volume (GMV) and ERN amplitude in individuals with high trait anxiety (N = 98) as well as the role of sex in moderating this association. The ERN was elicited from a flanker task, whereas structural MRI images were obtained from whole brain structural T1-weighted MRI scans. The results of voxel-based morphometry analyses showed that the relationship between ERN difference scores and GMV was moderated by sex in the dorsal anterior cingulate cortex (dACC). This sex difference was derived from a negative correlation between ERN difference scores and dACC GMV in females and a positive correlation in males. Our findings are in accordance with the critical role of the dACC serving as a neural substrate of error monitoring. It also provides further evidence for sex-specific associations with brain structures related to error monitoring.

Electroencephalography can provide advance warning of technical errors during laparoscopic surgery.

Intraoperative adverse events lead to patient injury and death, and are increasing. Early warning systems (EWSs) have been used to detect patient deterioration and save lives. However, few studies have used EWSs to monitor surgical performance and caution about imminent technical errors. Previous (non-surgical) research has investigated neural activity to predict future motor errors using electroencephalography (EEG). The present proof-of-concept cohort study investigates whether EEG could predict technical errors in surgery. In a large academic hospital, three surgical fellows performed 12 elective laparoscopic general surgeries. Audiovisual data of the operating room and the surgeon's neural activity were recorded. Technical errors and epochs of good surgical performance were coded into events. Neural activity was observed 40s prior and 10s after errors and good events to determine how far in advance errors were detected. A hierarchical regression model was used to account for possible clustering within surgeons. This prospective, proof-of-concept, cohort study was conducted from July to November 2021, with a pilot period from February to March 2020 used to optimize the technique of data capture and included participants who were blinded from study hypotheses. Forty-five technical errors, mainly due to too little force or distance (n = 39), and 27 good surgical events were coded during grasping and dissection. Neural activity representing error monitoring (p = .008) and motor uncertainty (p = .034) was detected 17s prior to errors, but not prior to good surgical performance. These results show that distinct neural signatures are predictive of technical error in laparoscopic surgery. If replicated with low false-alarm rates, an EEG-based EWS of technical errors could be used to improve individualized surgical training by flagging imminent unsafe actions-before errors occur and cause patient harm.

Inhibitory Control of Saccadic Eye Movements and Cognitive Impairment in Mild Cognitive Impairment

AbstractBackgroundMild cognitive impairment (MCI) may occur due to several forms of neurodegenerative diseases and non‐degenerative conditions and is associated with cognitive impairment that does not affect everyday activities. For a timely diagnosis of MCI to prevent progression to dementia, a screening tool of fast, low‐cost and easy access is needed. Recent research on eye movement hints it a potential application for the MCI screening. However, the precise extent of cognitive function decline and eye‐movement control alterations in patients with MCI is still unclear.MethodPatients with MCI (n = 79) and age‐matched cognitively healthy controls (HC) (n = 170) completed four saccadic eye‐movement paradigms: prosaccade (PS)/antisaccade (AS), Go/No‐go, and a battery of neuropsychological tests.ResultThe findings revealed significantly longer latency in patients with MCI than in HC during the PS task. Additionally, patients with MCI had a lower proportion of correct responses and a marked increase in inhibition errors for both PS/AS and Go/No‐go tasks. Furthermore, when patients with MCI made errors, they failed to self‐correct many of these inhibition errors. In addition to the increase in inhibition errors and uncorrected inhibition errors, patients with MCI demonstrated a trend toward increased correction latencies. We also showed a relationship between neuropsychological scores and correct and error saccade responses.ConclusionOur results demonstrate that, similar to patients with Alzheimer’s dementia (AD), patients with MCI generate a high proportion of erroneous saccades toward the prepotent target and fail to self‐correct many of these errors, which is consistent with an impairment of inhibitory control and error monitoring.

A new error-monitoring brain–computer interface based on reinforcement learning for people with autism spectrum disorders

Objective. Brain–computer interfaces (BCIs) are emerging as promising cognitive training tools in neurodevelopmental disorders, as they combine the advantages of traditional computerized interventions with real-time tailored feedback. We propose a gamified BCI based on non-volitional neurofeedback for cognitive training, aiming at reaching a neurorehabilitation tool for application in autism spectrum disorders (ASDs). Approach. The BCI consists of an emotional facial expression paradigm controlled by an intelligent agent that makes correct and wrong actions, while the user observes and judges the agent’s actions. The agent learns through reinforcement learning (RL) an optimal strategy if the participant generates error-related potentials (ErrPs) upon incorrect agent actions. We hypothesize that this training approach will allow not only the agent to learn but also the BCI user, by participating through implicit error scrutiny in the process of learning through operant conditioning, making it of particular interest for disorders where error monitoring processes are altered/compromised such as in ASD. In this paper, the main goal is to validate the whole methodological BCI approach and assess whether it is feasible enough to move on to clinical experiments. A control group of ten neurotypical participants and one participant with ASD tested the proposed BCI approach. Main results. We achieved an online balanced-accuracy in ErrPs detection of 81.6% and 77.1%, respectively for two different game modes. Additionally, all participants achieved an optimal RL strategy for the agent at least in one of the test sessions. Significance. The ErrP classification results and the possibility of successfully achieving an optimal learning strategy, show the feasibility of the proposed methodology, which allows to move towards clinical experimentation with ASD participants to assess the effectiveness of the approach as hypothesized.

Real-World Experience of Monitoring Practice of Endocrinopathies Associated with the Use of Novel Targeted Therapies among Patients with Solid Tumors.

Cancer treatments have gradually evolved into targeted molecular therapies characterized by a unique mechanism of action instead of non-specific cytotoxic chemotherapies. However, they have unique safety concerns. For instance, endocrinopathies, which are defined as unfavorable metabolic alterations including thyroid disorders, hyperglycemia, dyslipidemia, and adrenal insufficiency necessitate additional monitoring. The aim of this study was to assess the prevalence of monitoring errors and develop strategies for monitoring cancer patients who receive targeted therapies. A retrospective chart review was used to assess the prevalence of monitoring errors of endocrinopathies among cancer patients who received targeted therapies over one year. All of the adult cancer patients diagnosed with a solid tumor who received targeted therapies were included. The primary outcome was to determine the prevalence of monitoring errors of endocrinopathies. The secondary outcomes were to assess the incidences of endocrinopathies and referral practice to endocrinology services. A total of 128 adult patients with solid tumors were involved. The primary outcome revealed a total of 148 monitoring errors of endocrinopathies. Monitoring errors of the lipid profile and thyroid functions were the most common error types in 94% and 92.6% of the patients treated with novel targeted therapies, respectively. Subsequently, 57% of the monitoring errors in the blood glucose measures were identified. Targeted therapies caused 63 events of endocrinopathies, hyperglycemia in 32% of the patients, thyroid disorders in 15.6% of them and dyslipidemia in 1.5% of the patients. Our study showed a high prevalence of monitoring errors among the cancer patients who received targeted therapies which led to endocrinopathies. It emphasizes the importance of adhering to monitoring strategies and following up on the appropriate referral process.

Analytical method for the kinematic response and signal error of a downhole seismometer subjected to P-waves

This paper presents an analytical method for the steady-state kinematic response of downhole seismometers to vertically incident P-waves, based on a modified Vlasov model. The governing equations and boundary conditions of a soil–well system were obtained using Hamilton's principle. The three unknown functions were then decoupled using an iterative algorithm. The accuracy of the proposed method was validated by placing a downhole seismometer in the well and comparing the responses with previously reported results. A parametric study was performed to investigate the effects of the soil–well system properties on the kinematic response and monitoring errors of the downhole seismometer. Results indicated that the incident frequency, thickness of the foundation of the downhole seismometer, soil–well modulus ratio Ew/Es, and slenderness ratio of the downhole seismometer considerably influence the monitoring error, whereas soil damping has little effect on it.

Medication safety in the perioperative setting: A comparison of methods for detecting medication errors and adverse medication events.

The purpose of this study was to evaluate perioperative medication-related incidents (medication errors (MEs) and/or adverse medication events (AMEs)) identified by 2 different reporting methods (self-report and direct observation), and to compare the types and severity of incidents identified by each method. We compared perioperative medication-related incidents identified by direct observation in Nanji et al's 2016 study[1] to those identified by self-report via a facilitated incident reporting system at the same 1046-bed tertiary care academic medical center during the same 8-month period. Incidents, including MEs and AMEs were classified by type and severity. In 277 operations involving 3671 medication administrations, 193 MEs and/or AMEs were observed (5.3% incident rate). While none of the observed incidents were self-reported, 10 separate medication-related incidents were self-reported from different (unobserved) operations that occurred during the same time period, which involved a total of 21,576 operations and approximately 280,488 medication administrations (0.004% self-reported incident rate). The distribution of incidents (ME, AME, or both) did not differ by direct observation versus self-report methodology. The types of MEs identified by direct observation differed from those identified by self-report (P = .005). Specifically, the most frequent types of MEs identified by direct observation were labeling errors (N = 37; 24.2%), wrong dose errors (N = 35; 22.9%) and errors of omission (N = 27; 17.6%). The most frequent types of MEs identified by self-report were wrong dose (N = 5; 50%) and wrong medication (N = 4; 40%). The severity of incidents identified by direct observation and self-report differed, with self-reported incidents having a higher average severity (P < .001). The procedure types associated with medication-related incidents did not differ by direct observation versus self-report methodology. Direct observation captured many more perioperative medication-related incidents than self-report. The ME types identified and their severity differed between the 2 methods, with a higher average incident severity in the self-reported data.

Error-related brain activity: A time-domain and time-frequency investigation in pediatric obsessive-compulsive disorder.

Increased error-related negativity (ERN), a measure of error monitoring, has been suggested as a biomarker of obsessive-compulsive disorder (OCD). Additional insight into error monitoring is possible using time-frequency decomposition of electroencephalographic (EEG) data, as it allows disentangling the brain's parallel processing of information. Greater error-related theta is thought to reflect an error detection signal, while delta activity may reflect more elaborative post-detection processes (i.e., strategic adjustments). Recent investigations show that decreased error-related alpha may index attentional engagement following errors; additionally, increases and decreases in error-related beta could reflect motor inhibition and motor preparation, respectively. However, time-frequency dynamics of error monitoring in OCD are largely unknown. The present study examined time-frequency theta, delta, alpha and beta power in early adolescents with OCD using a data-driven, cluster-based approach. The aim was to explore electrocortical measures of error monitoring in early adolescents with (n=27, 15 females) and without OCD (n=27, 14 females) during an arrowhead version of the flanker task while EEG activity was recorded. Results indicated that the OCD group was characterized by increased ERN and error-related theta, as well as reduced error-related beta power decrease (i.e., greater power) compared to participants without OCD. Greater error-related beta explained variance in OCD over and above the ERN and error-related theta. By examining separate time-frequency measures, the present study provides novel insights into the dynamics of error monitoring, suggesting that pediatric OCD may be characterized by enhanced error monitoring (i.e., greater theta power) and post-error inhibition (i.e., reduced beta power decrease).

Online monitoring and active control of alignment errors in a tiled grating assembly using single wedge plate

With reference to application in chirped pulse amplification (CPA) based high-energy, ultra-short laser systems, online monitoring of alignment errors and active coherent locking of gratings in a tiled grating assembly (TGA) consisting of two gratings, each of size 140 mm x 120 mm and groove density 1740 grooves/mm, is presented. All the tiling alignment errors present in the TGA have been monitored simultaneously using a single wedge plate based lateral shearing interferometry and corrected using piezo motor driven 6-axis aligner stage. As a novelty, to have further advantages in the wedge plate based method for TGA alignment, the He-Ne laser beam diffracted/ reflected from the TGA has been folded back towards the source, which facilitated measurement sensitivity enhancement by a factor of two, and also generation and simultaneously capturing of both reflected and diffracted beam's interferograms using comparatively smaller dimension single wedge plate and CCD camera. The angular and translational alignment errors of the TGA have been actively locked in the range of ± 0.5 µrad and ± 50 nm respectively.

Analysis of Hadoop log file in an environment for dynamic detection of threats using machine learning

Log files are a significant piece of records that once properly analyzed, can yield helpful information. The usage of cloud services for log analysis is almost inevitable due to the greater rate of production and quantity of hardware and sensors that produce logs. The idea is to adopt machine learning mechanisms to “understand” what such a test suite's “anticipated” behavior is. This implies the system needs to be able to learn the difference among a Hadoop log file that contains a threat that does not really, based on past patterns. Many programs that really can effectively monitor the Hadoop cluster have been developed. The bulk of these tools collect relevant information from every cluster node and send it to be processed. The majority of these diagnostic tools were post-execution techniques commonly. This paper gives an experimental evaluation of the various log analysts used in Hadoop for error monitoring and detection. MapReduce is configured using these files. The algorithms were tested on a parser set of labelled log files, and their efficiency was assessed by looking for abnormal events in the Hadoop log files and executing an experimentation using the methods. The algorithms used were Term Frequency Inverse Document Frequency, Random Forest, and Local Outlier Factor. Later, the clustering with K-Means and Principal component analysis is utilized to extract certain relevant details from the data, monitor groups of bits of data to find aberrant events. The Term Frequency Inverse Document Frequency strategy surpassed the other two techniques in predicting threat occurrences in data, according to the findings. The findings would help developers in locating abnormal occurrences without having to go through the Hadoop log file row by row individually. The framework generates events that behave in a different way than the rest of such events in the log and leads the mechanism to fail to work.

The role of the attachment relation in the pathogenesis of auditory hallucinations – literature overview

Objective: The purpose of the article is to provide an overview of the literature concerning the role of the attachment relation in the pathogenesis of auditory hallucinations. The article holistically integrates the knowledge about the attachment relation with cognitive and biological factors. Materials and method: The literature overview was compiled using the following databases: EBSCO, PsycINFO, PubMed, Google Scholar. The databases were searched using the following keywords: „secure”, „disorganized”, „epigenetic”, „genetic”, „auditory hallucinations” and the term „monoaminergic system”. These words were combined with the term „attachment style”. Results: The literature analysis suggests that neurobiological and cognitive factors mediate in the pathomechamism affiliating traumatic experience in the attachment relation with auditory hallucinations These include: dysregulation within the monoaminergic systems, atrophy of the temporal areas and cognitive distortions such as source monitoring errors and negative automatic thoughts. Discussion and conclusions: Cognitive dysfunctions along with hereditary genetic susceptibility to auditory hallucinations may constitute factors distorting the correct interpretation of traumatic intrusions of relational experience. Future research should verify to what extent auditory hallucinations are the memory marks of a relational trauma. Verification of these hypotheses will enable better understanding of the role of the attachment styles in the development of auditory hallucinations and the formation of convictions about them.

Metric error monitoring for a cleaner record of timing.

A recent line of research has shown that humans can keep track of the direction and magnitude of their timing errors without relying on feedback. But these studies tested temporal error monitoring explicitly by interrogating participants regarding their errors, which might have inadvertently primed the prospective coupling between the first-order timing and second-order metacognitive judgments. The current study utilized an indirect way of testing temporal error awareness while providing a strong objective incentive for maximizing the accuracy of first-order timing performance. In two experiments, participants were asked to maximize the average proximity of their time reproduction to the target by accepting or rejecting their time reproduction depending on the subjective judgment of their proximity to the target time interval. We found that participants more frequently opted out of a trial with a larger distance between their reproductions and the target time interval in both directions, forming a positive quadratic relationship with reproduced time. Resultantly, timing precision was lower in trials that participants opted out of. Our results provide new evidence in support of the temporal error-monitoring performance of human participants. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism.

Traditional targeted metabolomic investigations identify a pre-defined list of analytes in samples and have been widely used for decades in the diagnosis and monitoring of inborn errors of metabolism (IEMs). Recent technological advances have resulted in the development and maturation of untargeted metabolomics: a holistic, unbiased, analytical approach to detecting metabolic disturbances in human disease. We aim to provide a summary of untargeted metabolomics [focusing on tandem mass spectrometry (MS-MS)] and its application in the field of IEMs. Data for this review was identified through a literature search using PubMed, Google Scholar, and personal repositories of articles collected by the authors. Findings are presented within several sections describing the metabolome, the current use of targeted metabolomics in the diagnostic pathway of patients with IEMs, the more recent integration of untargeted metabolomics into clinical care, and the limitations of this newly employed analytical technique. Untargeted metabolomic investigations are increasingly utilized in screening for rare disorders, improving understanding of cellular and subcellular physiology, discovering novel biomarkers, monitoring therapy, and functionally validating genomic variants. Although the untargeted metabolomic approach has some limitations, this "next generation metabolic screening" platform is becoming increasingly affordable and accessible. When used in conjunction with genomics and the other promising "-omic" technologies, untargeted metabolomics has the potential to revolutionize the diagnostics of IEMs (and other rare disorders), improving both clinical and health economic outcomes.