Missing Data Sets Research Articles

Multivariate Decision Tree-Oriented Early Warning Method for College Students’ Psychological Crisis Behavior

To prevent the occurrence of psychological crisis behavior and help college students grow from the crisis is not only an important issue related to college students’ mental health, but also a very necessary and urgent practical issue related to college students’ family happiness, college talent training, and social harmony and stability. Therefore, a multivariable decision tree-oriented early warning method for college students’ psychological crisis behavior is proposed. Using the laser beam emitted by the sensor as a reference standard, the psychological crisis behavior data is extracted, converted into different formats, divided into linear regions, and used to build a multivariate decision tree model. The model is used as the judgment processing target, and the judgment equation of mental health status data is established. The status data is divided into different processing groups as the processing object, calculate the maximum state value of the state data in the two data combinations to obtain the optimal early warning result, and finally realize the early warning of psychological crisis behavior. The experimental results show that the final success rate of early warning obtained by this method is high, which can improve the efficiency of early warning; the number of missing data sets was the least, and most of the subjects’ psychological risk tolerance was at the medium level.

Impact of Global Country Indicators on Life Expectancy

Investigating the factors that influence life expectancy is crucial for improving public health, planning healthcare and social services, and pension provision. Understanding these factors enables the development and implementation of effective policies to enhance the quality of life. This article presents a study that analyzes data from 195 countries, covering demographic, economic, healthcare, and education factors, to identify those that impact life expectancy. Various models were experimented with, including linear regression, random forest, gradient boosting, SVM, and artificial neural networks. Different machine learning algorithms, combinations of features, and strategies for handling missing dataset values were tested in building the models. The best-performing model used linear regression. It was found that reproductive characteristics, the number of individuals enrolled in higher education, and the number of general practitioners had the highest correlation with life expectancy. Surprisingly, out-of-pocket health expenditure was not a leading factor in determining life expectancy.

Antimicrobial efficacy of chitosan versus sodium hypochlorite: A systematic review and meta-analysis.

This meta-analysis aimed to compare the antibacterial efficacy of chitosan/chitosan nanoparticles (Ch/Ch-NPs) versus sodium hypochlorite/chlorhexidine (NaOCl/CHX). A search was performed in four electronic databases until December 08, 2023. Studies with missing, unclear, and insufficient data sets were excluded. The included studies were assessed by two independent reviewers using the Joanna Briggs Institute Critical Appraisal Checklist for Quasi-Experimental Studies. The meta-analysis of standardized mean difference was performed using a random effects model. Additionally, funnel plots as well as Egger's regression intercept test were used to evaluate potential publication bias. A total of 426 samples were used in nine included studies. There was no difference in antibacterial efficacy between Ch/Ch-NPs-NaOCl (SMD: 0.005; 95% CI: -0.844-0.854; p = 0.990). However, the antibacterial efficacy of NaOCl was statistically more effective than Ch/Ch-NPs (SMD: 0.807; 95% CI: 0.015-1.599; p = 0.046) using the bacterial culture method, and Ch/Ch-NPs was statistically higher than NaOCl (SMD: -1.827; 95% CI: -2.720, -0.934; p < 0.000) using confocal laser scanning microscopy. Ch/Ch-NPs may be an alternative to NaOCl against Enterococcus faecalis. The methods used in the invitro studies evaluating the antibacterial efficacy of irrigation solutions against E. faecalis may have had an impact on the results.

How estimating nuisance parameters can reduce the variance (with consistent variance estimation).

We often estimate a parameter of interest when the identifying conditions involve a finite-dimensional nuisance parameter . Examples from causal inference are inverse probability weighting, marginal structural models and structural nested models, which all lead to unbiased estimating equations. This article presents a consistent sandwich estimator for the variance of estimators that solve unbiased estimating equations including which is also estimated by solving unbiased estimating equations. This article presents four additional results for settings where solves (partial) score equations and does not depend on . This includes many causal inference settings where describes the treatment probabilities, missing data settings where describes the missingness probabilities, and measurement error settings where describes the error distribution. These four additional results are: (1) Counter-intuitively, the asymptotic variance of is typically smaller when is estimated. (2) If estimating is ignored, the sandwich estimator for the variance of is conservative. (3) A consistent sandwich estimator for the variance of . (4) If with the true plugged in is efficient, the asymptotic variance of does not depend on whether is estimated. To illustrate we use observational data to calculate confidence intervals for (1) the effect of cazavi versus colistin on bacterial infections and (2) how the effect of antiretroviral treatment depends on its initiation time in HIV-infected patients.

Discrimination of missing data types in metabolomics data based on particle swarm optimization algorithm and XGBoost model

In the field of data analysis, it is often faced with a large number of missing values, especially in metabolomics data, this problem is more prominent. Data imputation is a common method to deal with missing metabolomics data, while traditional data imputation methods usually ignore the differences in missing types, and thus the results of data imputation are not satisfactory. In order to discriminate the missing types of metabolomics data, a missing data classification model (PX-MDC) based on particle swarm algorithm and XGBoost is proposed in this paper. First, the missing values in a given missing data set are obtained by panning the missing values to obtain the largest subset of complete data, and then the particle swarm algorithm is used to search for the concentration threshold of missing data and the proportion of low concentration deletions as a percentage of overall deletions. Next, the missing data are simulated based on the search results. Finally, the training data are trained using the XGBoost model using the feature set proposed in this paper in order to build a classifier for the missing data. The experimental results show that the particle swarm algorithm is able to match the traditional enumeration method in terms of accuracy and significantly reduce the search time in concentration threshold search. Compared with the current mainstream methods, the PX-MDC model designed in this paper exhibits higher accuracy and is able to distinguish different deletion types for the same metabolite. This study is expected to make an important breakthrough in metabolomics data imputation and provide strong support for research in related fields.

A cosmic-ray database update: CRDB v4.1

The cosmic-ray database, CRDB, has been gathering cosmic-ray data for the community since 2013. We present a new release, CRDB v4.1, providing many new quantities and data sets, with several improvements made on the code and web interface, and with new visualisation tools. CRDB relies on the MySQL database management system, jquery and table-sorter libraries for queries and sorting, and PHP web pages and AJAX protocol for displays. A REST interface enables user queries from command line or scripts. A new (pip-installable) CRDB python library is developed and extensive jupyter notebook examples are provided. This release contains cosmic-ray dipole anisotropy data, high-energy p¯/p\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\bar{p}/p$$\\end{document} upper limits, some unpublished LEE and AESOP lepton time series, many more ultra-high energy data, and a few missing old data sets. It also includes high-precision data from the last three years, in particular the hundreds of thousands AMS-02 and PAMELA data time series (time-dependent plots are now enabled). All these data are shown in a gallery of plots, which can be easily reproduced from the public notebook examples. CRDB contains 316,126 data points from 504 publications, in 4111 sub-experiments from 131 experiments.

Tensor completion algorithm-aided structural color design.

In recent years, structural color has developed rapidly due to its distinct advantages, such as low loss, high spatial resolution and environmental friendliness. Various inverse design methods have been extensively investigated to efficiently design optical structures. However, the optimization method for the inverse design of structural color remains a formidable challenge. Traditional optimization approaches, such as genetic algorithms require time-consuming repetitions of structural simulations. Deep learning-assisted design necessitates prior simulations and large amounts of data, making it less efficient for systems with a small number of features. This study proposes a tensor completion algorithm capable of swiftly and accurately predicting missing datasets based on partially obtained datasets to assist in structural color design. Transforming the complex physical problem of structural color design into a spatial structure relationship problem linking geometric parameters and spectral data. The method utilizes tensor multilinear data analysis to effectively capture the complex relationships associated with geometric parameters and spectral data in higher-order data. Numerical and experimental results demonstrate that the algorithm exhibits high reliability in terms of speed and accuracy for diverse structures, datasets of varying sizes, and different materials, significantly enhancing design efficiency. The proposed algorithm offers a viable solution for inverse design problems involving complex physical systems, thereby introducing a novel approach to the design of photonic devices. Additionally, numerical experiments illustrate that the structural color of cruciform resonators with diamond can overcome the high loss issues observed in traditional dielectric materials within the blue wavelength region and enhance the corrosion resistance of the structure. We achieve a wide color gamut and a high-narrow reflection spectrum nearing 1 by this structure, and the theoretical analysis further verifies that diamond holds great promise in the realm of optics.

Filling of missing values of voltage data based on ResIOFNN

With the increasing scale of power Systems, the amount of data that needs to be collected increases exponentially, and data collection equipment will inevitably experience different degrees of data loss. Traditional missing data filling algorithms, such as Expectation Maximization Algorithm (EM) and K Nearest Neighbors (KNN), have low accuracy when dealing with missing data. Given the limitations of the current time series data-filling model, this paper combines the idea of deep learning technology. It proposes an improved voltage missing value-filling algorithm based on the Fourier neural network model. The model can combine the future and past information of the missing data to complete the filling work on the missing data set, which improves the precision of voltage data filling. The calculation example adopts the data of the real power grid for simulation analysis, and the calculation outcomes prove the data-filling method’s high level of fill accuracy.

Addressing missing data in the estimation of time-varying treatments in comparative effectiveness research.

Comparative effectiveness research is often concerned with evaluating treatment strategies sustained over time, that is, time-varying treatments. Inverse probability weighting (IPW) is often used to address the time-varying confounding by re-weighting the sample according to the probability of treatment receipt at each time point. IPW can also be used to address any missing data by re-weighting individuals according to the probability of observing the data. The combination of these two distinct sets of weights may lead to inefficient estimates of treatment effects due to potentially highly variable total weights. Alternatively, multiple imputation (MI) can be used to address the missing data by replacing each missing observation with a set of plausible values drawn from the posterior predictive distribution of the missing data given the observed data. Recent studies have compared IPW and MI for addressing the missing data in the evaluation of time-varying treatments, but they focused on missing confounders and monotone missing data patterns. This article assesses the relative advantages of MI and IPW to address missing data in both outcomes and confounders measured over time, and across monotone and non-monotone missing data settings. Through a comprehensive simulation study, we find that MI consistently provided low bias and more precise estimates compared to IPW across a wide range of scenarios. We illustrate the implications of method choice in an evaluation of biologic drugs for patients with severe rheumatoid arthritis, using the US National Databank for Rheumatic Diseases, in which 25% of participants had missing health outcomes or time-varying confounders.

Lodgers, Boarders, Trolley Patients; A Growing Challenge in Emergency Departments (ED). Zero Tolerance for Trolleys or Zero Tolerance for Zero Trolleys; A Crude Examination of Progressive Capacity Issues in an Irish Emergency Department.

Introduction:The Health Information and Quality Authority (HIQA) Tallaght Report of 2012 found care of lodged admitted patients on ED trolleys was undermined in terms of quality and safety. HIQA advised the practice of lodging in ED adjacent hospital corridors should be discontinued entirely. This message was reiterated during the pandemic. Some lodged patients may spend the total duration of their admission on an ED trolley. ED has 15 Adult rooms, seven pediatric rooms, two minor injury rooms, one procedure room and two resus bays. The aim was to calculate the annual number of days when no admitted patients were lodged on trolleys in ED.Method:A descriptive study using data available from nationally issued reports on patients allocated to trolleys to the ED of Wexford General Hospital from January 2019-September 2022. Data was collected from national HSE daily SBAR reports. “Lodged patients” were those present in ED admitted but for whom no ward bed existed at 0745 daily.Results:Data was collected for 1,369 days, 90 days were excluded due to missing data sets, and data were included for 1,279 days. 290 days were recorded in 2019 with no lodged patients, 126 in 2020, 55 in 2021, and only 11 days in 2022 with no lodged patients. In 2022 the average number of lodged patients was six (Range 0-19). A total of 47 days had a lodged count of ten or greater.Conclusion:Despite a strong recommendation from HIQA to terminate the practice of ED patient lodging, this has not been implemented. During the COVID-19 pandemic, there had been a reduction in the overall number of patients visiting the ED. This contributed to the reduction in trolley-lodged patients however post-COVID pandemic there has been a surge in attendance with a clear deficit in bed capacity.

Under Pressure–TrolleyGar, a Metric Reflecting a Hospital System at Crisis-Capacity

Introduction:Health service capacity has been an issue in Ireland since the 1980s swinging cuts. Government reports from 2003 have consistently identified a requirement for 3,000-5,000 extra beds on top of the current approximately 10,500 capacity. Acute hospital bed capacity issues have escalated, the formal system of recording “over capacity” patients or “patients on trolleys” has developed. A “Trolleygar” reports issues from the Health Service Executive (HSE) three times daily. This count is an underestimate as patients temporarily housed in day care units, surgical, or medical assessment units, discharge lounges and other clinical areas which have a bed space are not counted in this overcapacity measure. This study's aim is to calculate the annual number of days on which no patients were lodged on trolleys in Wexford General Hospital.Method:Descriptive study using anonymized freely available data from the national HSE Trolley GAR reports on trolley patients in Wexford General Hospital from January 2019 until September 2022. A Golden Zero trolley day was stated as a day on which there were no reported trolley-patients at the three time points, Silver Zero trolley day when two of the time periods recorded no trolleys and a Bronze Zero Trolley day when one period recorded a zero trolley count.Results:Data was collected on 1,369 days, with 90 days excluded due to missing data sets. There were 162 Golden days recorded (12.67% of total days). The year 2020 recorded the highest number of Golden days at 28.69% (105 days), followed by 2021 with 11.23% (41 days). During 2019 there were 3.84% (14 days) Golden days and 2022 had the lowest number (January-September) with 0.73% (2 days).Conclusion:Despite a zero-tolerance policy, Golden days are disappearing rapidly, capacity is urgently required with post-pandemic ED attendance surges worldwide. True recording of overcapacity patients is required for appropriate capacity modeling.

Attention-based message passing and dynamic graph convolution for spatiotemporal data imputation

Although numerous spatiotemporal approaches have been presented to address the problem of missing spatiotemporal data, there are still limitations in concurrently capturing the underlying spatiotemporal dependence of spatiotemporal graph data. Furthermore, most imputation methods miss the hidden dynamic connection associations that exist between graph nodes over time. To address the aforementioned spatiotemporal data imputation challenge, we present an attention-based message passing and dynamic graph convolution network (ADGCN). Specifically, this paper uses attention mechanisms to unify temporal and spatial continuity and aggregate node neighbor information in multiple directions. Furthermore, a dynamic graph convolution module is designed to capture constantly changing spatial correlations in sensors utilizing a new dynamic graph generation method with gating to transmit node information. Extensive imputation tests in the air quality and traffic flow domains were carried out on four real missing data sets. Experiments show that the ADGCN outperforms the state-of-the-art baseline.

Model Selection with Missing Data Embedded in Missing-at-Random Data

When models are built with missing data, an information criterion is needed to select the best model among the various candidates. Using a conventional information criterion for missing data may lead to the selection of the wrong model when data are not missing at random. Conventional information criteria implicitly assume that any subset of missing-at-random data is also missing at random, and thus the maximum likelihood estimator is assumed to be consistent; that is, it is assumed that the estimator will converge to the true value. However, this assumption may not be practical. In this paper, we develop an information criterion that works even for not-missing-at-random data, so long as the largest missing data set is missing at random. Simulations are performed to show the superiority of the proposed information criterion over conventional criteria.

Transfer Learning With Deep Neural Network for Capacity Prediction of Li-Ion Batteries Using EIS Measurement

In this study, transfer learning (TL) technique is used in conjunction with deep neural network (DNN) to predict the capacity of lithium-ion batteries. First, the base DNN model is trained and validated based on the source dataset containing electrochemical impedance spectroscopy (EIS) measurement at temperatures of 25 °C and 35 °C. Then, the base DNN model is retrained and validated using different proportions, i.e., the first 50% and 20% of the target dataset, which contains EIS measurement at the temperature of 45 °C. This will create a new model called DNN-TL carrying the knowledge from the base model. The DNN-TL model is used to predict the second proportions, i.e., the second 50% and 80% of the target dataset considered as missing data. The maximum mean absolute percentage error (MAPE), when the first 50% and 20% of the target dataset are used for retraining DNN-TL with no fixed-layer, is found to be 0.605% and 0.999%, respectively, which indicates the accuracy of the model to estimate the capacity of batteries. The average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> -squared of 0.9683 is achieved by DNN-TL with no fixed-layer indicating the goodness of its fit and its capability to follow the actual missing datasets.

Using multiple imputation of real-world data to estimate clinical remission in pediatric inflammatory bowel disease.

Aim: To evaluate the performance of the multiple imputation (MI) method for estimating clinical effectiveness in pediatric Crohn's disease in the ImproveCareNow registry; to address the analytical challenge of missing data. Materials & methods: Simulation studies were performed by creating missing datasets based on fully observed data from patients with moderate-to-severe Crohn's disease treated with non-ustekinumab biologics. MI was used to impute sPCDAI remission statuses in each simulated dataset. Results: The true remission rate (75.1% [95%CI: 72.6%, 77.5%]) was underestimated without imputation (72.6% [71.8%, 73.3%]). With MI, the estimate was 74.8% (74.4%, 75.2%). Conclusion: MI reduced nonresponse bias and improved the validity, reliability, and efficiency of real-world registry data to estimate remission rate in pediatric patients with Crohn's disease.

CURIES: Compendium of uranium Raman and infrared experimental spectra

Abstract Identification of radioactive materials is a critical goal of resource exploration, basic actinide science, and nuclear forensics, and we provide here new insights toward rapid, nondestructive analysis of uranium-containing minerals and technogenic phases. Raman and infrared spectroscopic data are powerful indicators of solid-phase U(VI) coordination chemistry. In addition, U(VI) minerals exhibit high chemical and structural diversity as artifacts of geochemical processes leading to ore formation. Spectral signals of axial UO22+ (U-Oyl) bond lengths and the influences of additional oxyanions on these values are well documented for uranium oxide and oxysalt minerals and technogenic phases. Additional insight regarding the underlying crystallographic structure and chemical composition of uranium materials can be extracted through a survey of all available Raman spectroscopic data for these phases. To this end, we have developed the Compendium of Uranium Raman and Infrared Experimental Spectra (CURIES). CURIES was compiled via a thorough review of literature and databases, and for mineral species that lack measured and recorded spectra, data were obtained either from museum and academic collections or by direct syntheses. Characteristic Raman spectroscopic features for subgroups of uranyl minerals within CURIES were elucidated using multivariate statistical analyses. In addition, average spectra for groups of uranyl minerals were determined, providing insight into common spectroscopic characteristics that are indicative of the structural origins from which they arise. As of publication, 275 mineral species and technogenic phases have been entered in CURIES, and of these, 83 phases have published spectra that have been included in the CURIES database. Data collection is ongoing, and we have triaged missing data sets to assess CURIES for completion and to identify mineral groups that lack representation and should therefore be prioritized for data acquisition and inclusion in the database.

Moving towards a taxonomy of cognitive impairments in epilepsy: application of latent profile analysis to 1178 patients with temporal lobe epilepsy.

In efforts to understand the cognitive heterogeneity within and across epilepsy syndromes, cognitive phenotyping has been proposed as a new taxonomy aimed at developing a harmonized approach to cognitive classification in epilepsy. Data- and clinically driven approaches have been previously used with variability in the phenotypes derived across studies. In our study, we utilize latent profile analysis to test several models of phenotypes in a large multicentre sample of patients with temporal lobe epilepsy and evaluate their demographic and clinical profiles. For the first time, we examine the added value of replacing missing data and examine factors that may be contributing to missingness. A sample of 1178 participants met the inclusion criteria for the study, which included a diagnosis of temporal lobe epilepsy and the availability of comprehensive neuropsychological data. Models with two to five classes were examined using latent profile analysis and the optimal model was selected based on fit indices, posterior probabilities and proportion of sample sizes. The models were also examined with imputed data to investigate the impact of missing data on model selection. Based on the fit indices, posterior probability and distinctiveness of the latent classes, a three-class solution was the optimal solution. This three-class solution comprised a group of patients with multidomain impairments, a group with impairments predominantly in language and a group with no impairments. Overall, the multidomain group demonstrated a worse clinical profile and comprised a greater proportion of patients with mesial temporal sclerosis, a longer disease duration and a higher number of anti-seizure medications. The four-class and five-class solutions demonstrated the lowest probabilities of a group membership. Analyses with imputed data demonstrated that the four-class solution was the optimal solution; however, there was a weak agreement between the missing and imputed data sets for the four-Class solutions (κ = 0.288, P < 0.001). This study represents the first to use latent profile analysis to test and compare multiple models of cognitive phenotypes in temporal lobe epilepsy and to determine the impact of missing data on model fit. We found that the three-phenotype model was the most meaningful based on several fit indices and produced phenotypes with unique demographic and clinical profiles. Our findings demonstrate that latent profile analysis is a rigorous method to identify phenotypes in large, heterogeneous epilepsy samples. Furthermore, this study highlights the importance of examining the impact of missing data in phenotyping methods. Our latent profile analysis-derived phenotypes can inform future studies aimed at identifying cognitive phenotypes in other neurological disorders.

Research on Remaining Useful Life Prediction Method of Rolling Bearing Based on Digital Twin.

Bearing is a key part of rotating machinery. Accurate prediction of bearing life can avoid serious failures. To address the current problem of low accuracy and poor predictability of bearing life prediction, a bearing life prediction method based on digital twins is proposed. Firstly, the vibration signals of rolling bearings are collected, and the time-domain and frequency-domain features of the actual data set are extracted to construct the feature matrix. Then unsupervised classification and feature selection are carried out by improving the self-organizing feature mapping method. Using sensitive features to construct a twin dataset framework and using the integrated learning CatBoost method to supplement the missing data sets, a complete digital twin dataset is formed. Secondly, important information is extracted through macro and micro attention mechanisms to achieve weight amplification. The life prediction of rolling bearing is realized by using fusion features. Finally, the proposed method is verified by experiments. The experimental results show that this method can predict the bearing life with a limited amount of measured data, which is superior to other prediction methods and can provide a new idea for the health prediction and management of mechanical components.

An Investigation of the Effect of Missing Data on Differential Item Functioning in Mixed Type Tests

In this research, the aim was to examine the effects of Markov Chain Monte Carlo (MCMC), multiple imputation (MI), and expectation maximization (EM), all methods of coping with missing data in mixed type tests including dichotomous and polytomous items, on the differential item functioning (DIF). The study was carried out on a complete data set consisting of the scores of 1160 students who took booklet number 9 in the science test in Trends in International Mathematics and Science Study (TIMSS) 2019 and answered it in full. The conditions to be examined for the effectiveness of the methods were missing data mechanism (MCAR and MAR), DIF level (A, B, and C), and missing data rate (10% and 20%). Data were assigned to the missing data sets created by deleting data at different rates under the missing completely at random (MCAR) and missing at random (MAR) mechanisms over the aforementioned data set. DIF analysis was performed on all the data sets obtained with the poly-SIBTEST method using the MCMC, MI, and EM methods. The results obtained from the complete data set were then compared with the result implications from other data sets for reference. The study showed that the EM and MCMC methods performed better for the C-level DIF than the A and B levels in terms of all conditions examined. MI was observed to be the most successful method in determining DIF in items showing DIF in 10% and 20% MCAR mechanisms. When compared with the complete data set, the three methods showed similar results in the 10% MAR mechanism while MCMC gave the closest results in the 20% MAR mechanism.

Re-Audit of Benzodiazepine and Z-Drug Prescribing in Two Community Treatment Teams in the Cumbria, Northumberland, Tyne and Wear Trust

AimsThis is a re-audit of Benzodiazepine and Z-hypnotic drugs prescriptions in two community treatment teams (CTTs) in the Cumbria Northumberland Tyne and Wear (CNTW) Trust, comparing with previous audits in 2009, 2017 and 2018 to check whether areas of good practice were maintained, and progress was made.MethodsWe reviewed caseloads of four CNTW consultants in the two CTTs which included 554 patient encounters with 60 encounters where benzodiazepines or z-drugs were prescribed. Nine missing data sets brought the total audit sample to 51. For these 51, prescribing information was gathered from RiO and assessed against standards derived from CNTW Trust Policy and BNF prescribing guidance. To be compliant, 90–100% of prescriptions needed to meet the standard.ResultsOverall, the rate of prescribing of benzodiazepines and Z- drugs increased from previous audit (7% in 2018, now 10.8%). Good areas of practice maintained were as follows (all 100%): all teams were compliant in prescribing within BNF limits, refrained from prescribing diazepam in 10 mg formulation, and no pregnant/post-partum women were prescribed these medications.Although non-compliant, there were clear improvements in documenting indicated use (2018: 61.65%, 2021: 80.8%), and providing prescriptions of &lt;4 weeks in duration (2018: 58.2%, 2021: 79.2%)Key areas of concern were as follows: poor documentation of indication, duration of treatment and plans for review/discontinuation (compliance ranged from 31.5% - 81.2% in these areas). There was poor documentation of what verbal advice was given (0–16.9%), and lack of clearly documented tapering/discontinuation plans for those on long-term prescriptions (16.1%). The provision of written advice reduced from previous audit (2018: 10.7%, 2021: 5.8%). As 41/51 encounters were via telephone or video due to COVID-19 pandemic, this may have impacted on results.ConclusionDespite improvement in some areas, there remains scope for ongoing improvement in other areas. To improve these, we plan to produce and distribute an educational email to all prescribers, including the following: information on this audit and its findings, prescribing guidelines, relevant e-links to patient information leaflets as well as the audit proforma used for this audit, to encourage prescribers to undertake self-directed practice. A poster will be distributed, highlighting prescribing guidelines and standards, to be printed and displayed in clinical areas as reminder of prescribing responsibilities and the importance of documentation. Prescribers will be encouraged to participate in a small quiz to test learning. Efficacy of these measures will be assessed with a re-audit in one years’ time.