Concurrent control chart pattern recognition in manufacturing processes based on zero-shot learning.

In real industrial settings, collecting and labeling concurrent abnormal control chart pattern (CCP) samples are challenging, thereby hindering the effectiveness of current CCP recognition (CCPR) methods. This paper introduces zero-shot learning into quality control, proposing an intelligent model for recognizing zero-shot concurrent CCPs (C-CCPs). A multiscale ordinal pattern (OP) feature considering data sequential relationship is proposed. Drawing from expert knowledge, an attribute description space (ADS) is established to infer from single CCPs to C-CCPs. An ADS is embedded between features and labels, and the attribute classifier associates the features and attributes of CCPs. Experimental results demonstrate an accuracy of 98.73 % for 11 unseen C-CCPs and an overall accuracy of 98.89 % for all 19 CCPs, without C-CCP samples in training. Compared with other features, the multiscale OP feature has the best recognition effect on unseen C-CCPs.

Sensitivity assessment of Biomphalaria glabrata (SAY, 1818) using reference substance sodium dodecyl sulfate for ecotoxicological analyzes.

Sodium dodecyl sulfate (SDS) is a surfactant used and recommended by regulatory agencies as a reference substance in ecotoxicological analyzes. In this work, acute toxicity assays were performed with adults and embryos of the freshwater snail Biomphalaria glabrata, an endemic organism with environmental and public health importance, to evaluate the effects of the surfactant and establish a sensitivity control chart. The organisms were exposed to SDS for 24 h to a range of concentrations, as well as a control group. Six assays were performed to establish the control chart for adults (with a median LC50 of 36.87 mg L-1) and differential sensitivity was observed at each embryonic stage (EC50 = blastulae 33.03, gastrulae 35.03, trochophore 39.71 and veliger 72.55 mg L-1). The following behavioral responses were observed in exposed adult snails: release of hemolymph and mucus, body outside the shell, and penile overexposure. Embryos at the blastulae and gastrulae stages were more sensitive, and teratogenic effects were accentuated in the trochophore stage. The difference in results obtained between adults and embryos underscore the importance of carrying out analyzes at different developmental stages. The serial assays established with SDS for B. glabrata demonstrated efficiency and constancy conditions in the assays with good laboratory practice standards. The wide distribution of Biomphalaria species in several countries, their easy maintenance and cultivation in the laboratory, in addition to ecological importance, make them economical alternatives for ecotoxicological assays.

Effect of Intra-articular Atelocollagen Injections for Patients With Knee Osteoarthritis: A Retrospective Chart Review.

Background Osteoarthritis (OA) is a prevalent and exhausting condition often requiring long-term management. While there is a steady growth in the use of collagen-based treatment for OA, there is a lack of studies assessing the safety and efficacy of repeated administration of injectable atelocollagen for OA. Objective This study aims to evaluate the clinical efficacy and safety of repeated administration of injectable atelocollagen in reducing knee pain for patients with knee OA. Methods Clinical records of 91 patients from five hospitals were reviewed for this retrospective study. All 91 patients had received repeated administration of injectable atelocollagen (CartiPRO®, Dalim Tissen Co., Ltd., South Korea) as a treatment for knee OA for seven months. The efficacy of injectable atelocollagen was evaluated by physicians at least 30 days after the last administration, with outcomes categorized as "effective", "moderately effective", or "not effective". For analysis purposes, both "effective" and "moderately effective" were grouped as "effective" while "not effective" was classified as "ineffective". Safety was assessed by monitoring the incidence of adverse events (AEs) reported within six months following the re-administration of atelocollagen. Results Among the 91 patients, 96.7% (88 patients) experienced effective pain relief following the re-administration of CartiPRO®, with 3.3% (three patients) reporting ineffectiveness. In terms of safety assessment, 35 patients reported AEs, totaling up to 44 events, with no serious or unexpected device-related AEs. Conclusion The repeated use of atelocollagen was found to be both safe and effective in managing knee pain for patients with knee OA. These findings support the repeated use of injectable atelocollagen as a reliable treatment option for managing knee OA pain in clinical practice.

Root Cause Analysis of Increased Referral Rates in a Sub-district Hospital, Tamil Nadu: A Quality Improvement Initiative.

Background Sub-district hospitals in Tamil Nadu are critical in providing essential healthcare services, but they face significant challenges that can lead to increased patient referrals to higher-level facilities. High referral rates can overburden tertiary care centers, delay specialized treatment, and affect patient outcomes. This study aims to identify the root causes of increased referral rates in a sub-district hospital and implement targeted interventions to reduce unnecessary referrals. Methods A descriptive study was conducted at Sriperumbudur sub-district hospital in Tamil Nadu from May to August 2023. The study utilized a root cause analysis (RCA) approach, incorporating qualitative data from brainstorming sessions with healthcare providers and administrative staff, and quantitative data from hospital records on referral rates. A fishbone (Ishikawa) diagram was employed to map causal factors, and Pareto and bar charts were used to analyze and present referral trends. Interventions were implemented using the Plan-Do-Study-Act (PDSA) cycle. Results The analysis identified several key factors contributing to high referral rates, including inadequate diagnostic services, insufficient staffing, and lack of essential resources such as CT scans and blood components. Following targeted interventions, referral rates decreased significantly from 101 cases in May-June 2023 to 52 cases in July-August 2023 highlighting a reduction of over 48%. The most notable reductions were seen in referrals for road traffic accidents with head injury (38.7%) reduction, chronic kidney disease (CKD)/hypertension (HT)/diabetes mellitus (DM) (46.2%) reduction, and crush injuries (45.5%) reduction. Conclusions The RCA revealed systemic issues that were contributing to increased referral rates at the sub-district hospital. Implementing targeted interventions based on the RCA findings led to a significant reduction in referrals, improving patient care at the local level and alleviating the burden on tertiary care centers. This study underscores the importance of continuous quality improvement initiatives in strengthening healthcare delivery at the sub-district level.

New French height velocity growth charts: An innovative big-data approach based on routine measurements.

AIM: Height velocity is considered a key auxological tool to monitor growth, but updated height velocity growth charts are lacking. We aimed to derive new French height velocity growth charts by using a big-data approach based on routine measurements. METHODS: We extracted all growth data of children aged 1 month-18 years from the electronic medical records of 42 primary care physicians, between 1 January 1990 and 8 February 2018, throughout the French metropolitan territory. We derived annual and biannual height velocity growth charts until age 15 years by using the Lambda-Mu-Sigma method. These new growth charts were compared to the 1979 French and 2009 World Health Organisation (WHO) ones. RESULTS: New height velocity growth charts were generated with 193 124 and 209 221 annual and biannual values from 80 204 and 87 260 children, respectively, and showed good internal fit. Median curves were close to the 1979 French or 2009 WHO ones, but SD curves displayed important differences. Similar results were found with the biannual height velocity growth charts. CONCLUSION: We produced new height velocity growth charts until age 15 years by using a big-data approach applied to measurements routinely collected in clinical practice. These updated growth charts could help optimise growth-monitoring performance.

Cervical length evolution in pregnancy and prediction of preterm delivery.

PURPOSE: To construct reference charts for cervical length (CL) in pregnancy based on longitudinal measurements and to assess the value of measuring cervical length after 24 weeks of gestation. METHODS: CL was measured transvaginally in singleton pregnancies at 5 to 41 weeks. Pregnancies with more than one measurement were used for creating the CL chart, whereas any measurement after 24 weeks was considered for assessing the correlation of CL with preterm delivery. RESULTS: The dataset consisted of 12,601 pregnancies and 33,899 observations. Gestational age was the main predictor of CL (R2 = 67.3%). Further adjustments in the fixed effects part for maternal characteristics (age, BMI, smoking status, parity, history of miscarriage, and cervical surgery) improved the prediction ability (R2 = 78.4%). CL was predictive of spontaneous preterm delivery before 37 weeks when measured at any gestation between 24 and 35 weeks and it was predictive of spontaneous preterm delivery before 32 weeks when measured between 28 and 31 weeks (AUC = 0.68). CONCLUSION: Gestational age is the main predictor of CL followed by maternal characteristics and history. CL assessment between 24 and 35 + 6 weeks can enhance our ability to predict spontaneous preterm delivery.

Cardiovascular risk assessment using non-laboratory based WHO CVD risk prediction chart with respect to hypertension status among older Indian adults: insights from nationally representative survey.

Introduction: Knowledge of the risk of developing cardiovascular diseases (CVD) in the population is an important risk management strategy for the prevention of this disease. This is especially true for India, which has resource-restrained settings with an increased risk in a younger population for the development of the disease. An important modifiable risk factor for CVD is hypertension, with its influence on the development of CVD. Methods: The data from the first wave of the Longitudinal Ageing Study in India (LASI) was used to calculate the 10-year CVD Risk Score among older adults ≥45 years using a WHO (2019) non-laboratory- based chart for South Asia. Univariate analysis was done using Pearson's chi-square test, and multivariable analysis using ordinal logistic regression. Categories of CVD risk score were considered as dependent variable. Socio-demographic variables, regular exercise, history of diabetes and hyperlipidaemia were considered as the independent variables. Relationship between CVD Risk score and hypertensives and self-reported hypertensives were presented using restricted cubic splines. Result: Two-thirds (68.8%) of the population had a 10-year CVD risk of <10, and 2.8% had a risk of ≥20%. The self-reported hypertensives were distributed linearly in restricted cubic splines, with a more scattered distribution in higher scores, while actual hypertensives showed a sigmoid pattern. Urban residents (OR-0.88), being unmarried (OR-0.86), being in the richer (OR-0.94) and richest (OR-0.86) monthly per capita expenditure (MPCE) quintile and exercising regularly (OR-0.68) decreased the odds of being in a higher CVD risk score. Less than primary schooling (1.21) and diabetics (1.69) had higher odds for a higher CVD risk score. Conclusion: In this population, two-thirds had <10% risk for the development of CVD. The study shows a higher risk among rural, poor, and those with a lower education and lower CVD risk for those undertaking physical activity. The sigmoid pattern in actual hypertensives highlights the need for early detection. Even those with undiagnosed hypertension but with a higher BP had a similar risk for disease development, thus highlighting the need for an early detection of hypertension.

10-year risk for cardiovascular diseases according to the WHO prediction chart: findings from the National Health and Morbidity Survey (NHMS) 2019.

BACKGROUND: There has been a significant increase in the incidence of cardiovascular disease (CVD) in Malaysia. It is important to identify the group at high risk of CVD. This study aimed to assess the population distribution and factors associated with 10-year CVD risk among adults aged 40 to 74 years in Malaysia. METHODS: This study used secondary data from the NHMS 2019, a nationally representative cross-sectional population study. The following measurements were collected: anthropometric, systolic blood pressure, fasting blood glucose, total cholesterol, smoking, and sociodemographic. The 2019 WHO Southeast Asia laboratory-based charts were used to estimate individuals' CVD risk. These charts predict significant cardiovascular events over ten years. Multiple logistic regression analysis was employed to ascertain the factors that are linked to elevated or extremely elevated risk of CVD. RESULTS: A total of 5,503 respondents were included in the analysis. Less than one-quarter of the respondents were current smokers and obese. Approximately 41.7%, 30.9%, and 22.5% of the participants had extremely low risk (less than 5%), low risk (between 5% and less than 10%), and moderate risk (between 10% and less than 20%), respectively. A total of 4.9% of the participants were categorised as having high (20% to < 30%) or very high (CVD) risk (≥ 30%). This classification was more prevalent among males (7.3%) than among females (2.5%; p < 0.001). The factors associated with high/very high CVD risk were unemployment (aOR = 1.88, 95% CI = 1.47-2.40), those with non-formal and primary education level (aOR = 2.36, 95% CI = 1.36 - 4.12 and aOR = 3.28, 95% CI = 2.10 - 5.12, respectively), and being physically inactive with obesity (aOR = 2.19, 95% CI = 1.18 - 4.08). CONCLUSIONS: This study revealed that almost 5% of the population in Malaysia has a high 10-year CVD risk. These findings highlight Malaysia's urgent need for comprehensive CVD prevention efforts.

Sustainable development goals and green human resource management: A comprehensive review of environmental performance.

Sustainable development goals and rising sustainability concerns push stakeholders and society to demand organizations adopt innovative practices that can contribute to business sustainability. This study aims to provide a systematic review through comprehensive bibliometric analysis of almost 1st quarter of the 21st century. The main focus is on how green human resource management practices promote and have a relationship with environmental performance, which is a significant part of sustainability. The authors systematically reviewed 242 top-tier articles from Scopus and Web of Science databases by following the "preferred reporting items for systematic reviews and meta-analyses" methodology. A significant relationship began in 2015 and gained popularity after COVID-19. The studies suggest that management should embrace the most demanding green human resource management practices, such as green competence building, motivation enhancement, and employee involvement, for better environmental performance. So, organizations can help acquire and retain committed and eco-friendly employees whose ecological values and objectives are compatible with the organization. The study also provides insight into the role of green human resource management practices in fostering eco-friendly behaviour of employees that, in turn, influence environmental performance. Most authors used the theory of ability-motivation-opportunity, a supporting mechanism for how organizations protect the environment through adopting green human resource management practices. In the future, corporate environmental responsibility, environmental regulations intensity, pro-environmental behaviour, and green work-life balance are suggested mechanisms to enhance environmental performance through green human resource management practices.

Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review.

BACKGROUND: Postoperative infections remain a crucial challenge in health care, resulting in high morbidity, mortality, and costs. Accurate identification and labeling of patients with postoperative bacterial infections is crucial for developing prediction models, validating biomarkers, and implementing surveillance systems in clinical practice. OBJECTIVE: This scoping review aimed to explore methods for identifying patients with postoperative infections using electronic health record (EHR) data to go beyond the reference standard of manual chart review. METHODS: We performed a systematic search strategy across PubMed, Embase, Web of Science (Core Collection), the Cochrane Library, and Emcare (Ovid), targeting studies addressing the prediction and fully automated surveillance (ie, without manual check) of diverse bacterial infections in the postoperative setting. For prediction modeling studies, we assessed the labeling methods used, categorizing them as either manual or automated. We evaluated the different types of EHR data needed for the surveillance and labeling of postoperative infections, as well as the performance of fully automated surveillance systems compared with manual chart review. RESULTS: We identified 75 different methods and definitions used to identify patients with postoperative infections in studies published between 2003 and 2023. Manual labeling was the predominant method in prediction modeling research, 65% (49/75) of the identified methods use structured data, and 45% (34/75) use free text and clinical notes as one of their data sources. Fully automated surveillance systems should be used with caution because the reported positive predictive values are between 0.31 and 0.76. CONCLUSIONS: There is currently no evidence to support fully automated labeling and identification of patients with infections based solely on structured EHR data. Future research should focus on defining uniform definitions, as well as prioritizing the development of more scalable, automated methods for infection detection using structured EHR data.

Exploring ambulance clinicians' clinical reasoning when training mass casualty incidents using virtual reality: a qualitative study.

BACKGROUND: How ambulance clinicians (ACs) handle a mass casualty incident (MCI) is essential for the suffered, but the training and learning for the ACs are sparse and they don't have the possibility to learn without realistic simulation training. In addition, it is unclear what type of dilemmas ACs process in their clinical reasoning during an MCI. With virtual reality (VR) simulation, the ACs clinical reasoning can be explored in a systematic way. Therefore, the objective was to explore ambulance clinicians' clinical reasoning when simulating a mass casualty incident using virtual reality. METHODS: This study was conducted as an explorative interview study design using chart- stimulated recall technique for data collection. A qualitative content analysis was done, using the clinical reasoning cycle as a deductive matrix. A high-fidelity VR simulation with MCI scenarios was used and participants eligible for inclusion were 11 senior ACs. RESULTS/CONCLUSION: All phases of the clinical reasoning cycle were found to be reflected upon by the participants during the interviews, however with a varying richness of analytic reflectivity. Non-analytic reasoning predominated when work tasks followed specific clinical guidelines, but analytical reasoning appeared when the guidelines were unclear or non-existent. Using VR simulation led to training and reflection on action in a safe and systematic way and increased self-awareness amongst the ACs regarding their preparedness for MCIs. This study increases knowledge both regarding ACs clinical reasoning in MCIs, and insights regarding the use of VR for simulation training.

Factors Associated with Take Home Naloxone Refusal among Emergency Department Patients Participating in an Opioid Overdose Prevention Program.

BACKGROUND: Increasing the equitable distribution of take home naloxone (THN) may result in reduced deaths from opioid overdose (OD). OBJECTIVES: The primary study objective is to describe the demographic and clinical characteristics of emergency department (ED) patients who decline THN. The findings of this descriptive study may generate new hypotheses for successful THN distribution. METHODS: Retrospective chart review using prospectively collected program evaluation data from a single urban EDs Health Education THN database and electronic health record. Characteristics of participants who refused versus accepted THN were compared using Chi-square testing for categorical variables and t-tests for continuous variables. A multivariate model was built to assess associations of statistical and clinically relevant characteristics with THN refusal. RESULTS: A total of 711 ED patients were offered THN of which 334 (46%) declined. In unadjusted analysis, with the independent variable being refusal of the THN offer, being currently on medication for opioid use disorder (MOUD) was associated with a greater odds of refusal (OR 1.9, 95%CI 1.3-2.6) while any drug related overdose (OR 0.6, 95%CI 0.4-0.8) or being given a prescription for buprenorphine in the ED (OR 0.2, 95%CI 0.1-0.9) were both associated with a lower odds of refusal. CONCLUSIONS: Demographic characteristics did not differ between those who accept versus refuse THN. Patients already receiving MOUD were more likely to refuse THN while those starting MOUD in the ED were less likely to refuse THN. Further studies are needed to determine the root causes of patients' declination of THN and develop targeted interventions to address these causes.

Quality Control in RT-PCR Viral Load Assays: Evaluation of Analytical Performance for HIV, HBV, and HCV.

Introduction: Quality Control Management (QCM) in clinical laboratories is crucial for ensuring reliable results in analytical measurements, with biological variation being a key factor. The study focuses on assessing the analytical performance of the Reverse Transcription Polymerase Chain Reaction (RT-PCR) system for Human Immunodeficiency Virus (HIV), Hepatitis B (HBV), and Hepatitis C (HCV). Five models proposed between 1999 and 2014 offer different approaches to evaluating analytical quality, with Model 2 based on biological variation and Model 5 considering the current state of the art. The study evaluates the RT-PCR system's analytical performance through Internal Quality Control (IQC) and External Quality Control (EQC). Materials and Methods: The Laboratório Central de Saúde Pública do Estado do Ceará (LACEN-CE) conducted daily IQC using commercial kits, and EQC was performed through proficiency testing rounds. Random error, systematic error, and total error were determined for each analyte. Results: Analytical performance, assessed through CV and random error, met specifications, with HIV and HBV classified as "desirable" and "optimal." EQC results indicated low systematic error, contributing to total errors considered clinically insignificant. Conclusion: The study highlights the challenge of defining analytical specifications without sufficient biological variability data. Model 5 is deemed the most suitable. The analytical performance of the RT-PCR system for HIV, HBV, and HCV at LACEN-CE demonstrated satisfactory, emphasizing the importance of continuous quality control in molecular biology methodologies.

Utilizing Maternal Morbidity as a Novel Screening (MMS) Tool for Predicting Peripartum Morbidity at a Rural Tertiary Care Teaching Hospital in Central India.

Background The majority of complications and deaths related to childbirth are concentrated in developing and disadvantaged nations, where the rates are unacceptably elevated. These incidents predominantly occur in the vicinity during the intrapartum period and immediately after childbirth. The peripartum period is especially critical for expectant mothers, as it represents the time when a significant number of complications and deaths occur. This study aimed to develop, validate, and assess the efficacy of the maternal morbidity screening (MMS) tool for predicting peripartum morbidity. Methodology The study was conducted in two phases: Phase one involved developing, validating, and piloting the MMS tool, while Phase two focused on evaluating and comparing the MMS tool with the modified early obstetric warning system (MEOWS) chart for predicting peripartum morbidity. An observational analytical clinical study design was utilized. Result In Phase one, the MMS tool was developed and validated by subject experts, resulting in a reliability score of 0.90. Therefore, the tool was deemed reliable and valid. Phase two results revealed that obstetric morbidity in the maternal morbidity group was 66.66%, higher than the 32% observed with the MEOWS chart. The MMS tool demonstrated significantly higher sensitivity at 95.24%, specificity at 89.50%, and predictive value at 98.50%, yielding an overall accuracy of 90.50%. In comparison, the MEOWS chart exhibited a sensitivity of 70.51%, specificity of 86.81%, predictive value of 92.94%, and accuracy of 83.71%. Conclusion The occurrence of maternal morbidity in the trigger zone was significantly higher than in the non-trigger zone in the MMS tool. The MMS tool was significantly more effective as a predictor of peripartum morbidity compared to the MEOWS chart.

Validation of Titmus vision screener V4 for diagnosing refractory errors and colour blindness: An analogy with quotidian.

Background: This study aims at evaluating the validity profile of Titmus vision screener V4 for gauging visual acuity and colour perception as a screening tool. Methods: Eleven hundred young adults attending eye OPD at a tertiary care center were included in the study. All individuals underwent distance visual acuity (DVA) measurement with Snellen chart and Titmus vision screener V4, at 6 m on the same day. Later, these individuals underwent colour vision testing with Ishihara pseudo isochromatic plates and Titmus vision screener V4. Results: The age distribution ranged from 19 to 53 years with mean age as 26.6 years. Males (87.4%) were more as compared to females. Mean DVA measured by Snellen's chart and Titmus vision screener V4 were 0.088 ± 0.15 and 0.110 ± 0.17, respectively (p = 0.000). Mean difference between DVA by Snellen's chart and Titmus screener V4 was -0.021 ± 0.82 with 95% CI (-0.026 to -0.016). For measuring visual acuity, Titmus vision screener V4 showed a sensitivity of 99.5% and specificity of 90.6%. Negative predictive value and positive predictive value were 99.6% and 85.6%, respectively. For diagnosing colour perception, the Titmus vision screener V4 showed a sensitivity of 100% and specificity of 99.8%. The negative predictive value and positive predictive value were 100% and 88.8% respectively. Area under the curve of receiver operating characteristic (ROC) curve was 0.960 with 95%CI (0.949-0.972). Conclusion: Our study supports use of Titmus vision screener V4 as an excellent screening tool for measurement of visual acuity as well as colour perception.

Balancing the efforts of chart review and gains in PRS prediction accuracy: An empirical study.

OBJECTIVE: Phenotypic misclassification in genetic association analyses can impact the accuracy of PRS-based prediction models. The bias reduction method proposed by Tong et al. (2019) has demonstrated its efficacy in reducing the effects of bias on the estimation of association parameters between genotype and phenotype while minimizing variance by employing chart reviews on a subset of the data for validating phenotypes, however its improvement of subsequent PRS prediction accuracy remains unclear. Our study aims to fill this gap by assessing the performance of simulated PRS models and estimating the optimal number of chart reviews needed for validation. METHODS: To comprehensively assess the efficacy of the bias reduction method proposed by Tong et al. in enhancing the accuracy of PRS-based prediction models, we simulated each phenotype under different correlation structures (an independent model, a weakly correlated model, a strongly correlated model) and introduced error-prone phenotypes using two distinct error mechanisms (differential and non-differential phenotyping errors). To facilitate this, we used genotype and phenotype data from 12 case-control datasets in the Alzheimer's Disease Genetics Consortium (ADGC) to produce simulated phenotypes. The evaluation included analyses across various misclassification rates of original phenotypes as well as quantities of validation set. Additionally, we determined the median threshold, identifying the minimal validation size required for a meaningful improvement in the accuracy of PRS-based predictions across a broad spectrum. RESULTS: This simulation study demonstrated that incorporating chart review does not universally guarantee enhanced performance of PRS-based prediction models. Specifically, in scenarios with minimal misclassification rates and limited validation sizes, PRS models utilizing debiased regression coefficients demonstrated inferior predictive capabilities compared to models using error-prone phenotypes. Put differently, the effectiveness of the bias reduction method is contingent upon the misclassification rates of phenotypes and the size of the validation set employed during chart reviews. Notably, when dealing with datasets featuring higher misclassification rates, the advantages of utilizing this bias reduction method become more evident, requiring a smaller validation set to achieve better performance. CONCLUSION: This study highlights the importance of choosing an appropriate validation set size to balance between the efforts of chart review and the gain in PRS prediction accuracy. Consequently, our study establishes a valuable guidance for validation planning, across a diverse array of sensitivity and specificity combinations.

Automated Pain Spots Recognition Algorithm Provided by a Web Service-Based Platform: Instrument Validation Study.

Background: Understanding the causes and mechanisms underlying musculoskeletal pain is crucial for developing effective treatments and improving patient outcomes. Self-report measures, such as the Pain Drawing Scale, involve individuals rating their level of pain on a scale. In this technique, individuals color the area where they experience pain, and the resulting picture is rated based on the depicted pain intensity. Analyzing pain drawings (PDs) typically involves measuring the size of the pain region. There are several studies focusing on assessing the clinical use of PDs, and now, with the introduction of digital PDs, the usability and reliability of these platforms need validation. Comparative studies between traditional and digital PDs have shown good agreement and reliability. The evolution of PD acquisition over the last 2 decades mirrors the commercialization of digital technologies. However, the pen-on-paper approach seems to be more accepted by patients, but there is currently no standardized method for scanning PDs. Objective: The objective of this study was to evaluate the accuracy of PD analysis performed by a web platform using various digital scanners. The primary goal was to demonstrate that simple and affordable mobile devices can be used to acquire PDs without losing important information. Methods: Two sets of PDs were generated: one with the addition of 216 colored circles and another composed of various red shapes distributed randomly on a frontal view body chart of an adult male. These drawings were then printed in color on A4 sheets, including QR codes at the corners in order to allow automatic alignment, and subsequently scanned using different devices and apps. The scanners used were flatbed scanners of different sizes and prices (professional, portable flatbed, and home printer or scanner), smartphones with varying price ranges, and 6 virtual scanner apps. The acquisitions were made under normal light conditions by the same operator. Results: High-saturation colors, such as red, cyan, magenta, and yellow, were accurately identified by all devices. The percentage error for small, medium, and large pain spots was consistently below 20% for all devices, with smaller values associated with larger areas. In addition, a significant negative correlation was observed between the percentage of error and spot size (R=-0.237; P=.04). The proposed platform proved to be robust and reliable for acquiring paper PDs via a wide range of scanning devices. Conclusions: This study demonstrates that a web platform can accurately analyze PDs acquired through various digital scanners. The findings support the use of simple and cost-effective mobile devices for PD acquisition without compromising the quality of data. Standardizing the scanning process using the proposed platform can contribute to more efficient and consistent PD analysis in clinical and research settings.

Incidence and Impact on Quality of Life of Heavy Menstrual Bleeding in Women on Oral Anticoagulant Therapy.

INTRODUCTION: Heavy menstrual bleeding affects up to two thirds of women on oral anticoagulation. The rates of heavy menstrual bleeding, its impact on quality of life and associated risk factors in women attending anticoagulation clinics in South Africa are largely unknown. MATERIALS AND METHODS: A prospective cohort study was performed over an eight-month period in women on Warfarin (n = 30) and Rivaroxaban (n = 27) for a median [interquartile range] duration of 15.5 [78.0] months attending an anticoagulation clinic in Johannesburg, South Africa. Heavy menstrual bleeding was assessed over one menstrual cycle using the validated pictorial blood loss assessment charts (PBAC) and the menstrual bleeding questionnaire (MBQ). RESULTS: In this population of predominantly African ethnicity, with a median age of 39 [8] years, 39 (68.4%) women experienced heavy menstrual bleeding, defined as a PBAC score of >100. Median cycle length on anticoagulation and MBQ scores were significantly higher among women with a PBAC score of >100 (p > 0.05). Univariate analysis identified Rivaroxaban as a risk factor for heavy menstrual bleeding (OR 5.03, 95% CI 1.40-18.12). Heavy menstrual bleeding required treatment in 29 (74.4%) women which included management of iron deficiency, anti-fibrinolytics, modification of anticoagulation and hormonal contraception. CONCLUSION: Heavy menstrual bleeding was associated with a considerable negative impact on quality of life. This was most significant for women on Rivaroxaban as compared to Warfarin. It is essential to monitor and appropriately treat heavy menstrual bleeding in at risk women on anticoagulant treatment.

Feedback, Workshop, and Random Monitoring as Quality Assurance Interventions in Improving Data Entries of Residents in Electronic Medical Records of UP Health Service for COVID-19 Teleconsultations.

Background: Medical records provide a repository of patient information, physical examination, laboratory findings, and the outcomes of interventions. The completeness of data contained in the electronic medical record (EMR) is an important factor leading to health service improvement. Quality assurance (QA) activities have been utilized to improve documentation in electronic medical records. Objective: To determine the effectiveness of QA interventions (feedback, workshop, and random monitoring system) in improving completeness of data entries in the EMR of resident physicians for COVID-19 teleconsultations. Methods: This was a before-and-after study involving EMR entries of physician trainees on health care workers (HCWs) from March to October 2022 of the COVID-19 pandemic. A chart audit was conducted against a checklist of criteria for three months before and after the interventions. QA interventions included the provision of feedback on the results of the initial chart review; conducting a QA workshop on setting of standards, chart audit, data encoding, analysis, and presentation; and random monitoring/feedback of resident charting. The change in the level of completeness from pre- to post-intervention was computed, and the percentage of charts meeting the minimum standard of 90% completeness was likewise determined. Results: A total of 362 and 591 chart entries were audited before and after the interventions. The average percentage of completeness of medical records during initial consultation improved from 83% to 95% (p>0.05). The documentation of the reason for seeking consultation significantly increased from <1% to 84%. The reporting of past exposure and level of risk decreased to 89% (p=0.001) in the initial consult and 12% (p=0.001) in the fit-to-work, respectively. Majority of the criteria for work clearance improved after the intervention. However, the average completeness of entries did not reach 90% post-intervention for fit-to-work consultations. Conclusion: Feedback, quality assurance workshop, and random monitoring of electronic medical records are effective in increasing documentation practices for the chief complaint and dates of illness duration but showed non-significant increasing trend on overall percentage of EMR completeness for COVID-19 teleconsultations.

Carbon emission warning method for machine tool manufacturing process based on dynamic adaptive EWMA control chart.

Machine tools constitute the backbone of the industrial sector, representing the largest global inventory of equipment. The carbon emissions resulting from the production of each machine tool merit attention. Effective management of carbon emissions in the machine tool manufacturing process is crucial. This paper introduces a novel method for early carbon emission warnings in the machine tool manufacturing process, utilizing an adaptive dynamic exponentially weighted moving average (EWMA) approach. This method addresses the challenges in identifying and monitoring abnormal carbon emissions, emerging from uncertainties and dynamic correlations. Utilizing dynamic sampling techniques and adaptive principles, this method constructs an adaptive dynamic EWMA control chart. The EWMA control chart incorporates a multi-objective optimization design model, concentrating on carbon emissions in the machine tool manufacturing process, and incorporates statistical, economic, and environmental objectives. To mitigate slow convergence rates and enhance optimization accuracy in complex control chart multi-objective optimization algorithms, this study proposes an enhanced Harris hawks optimization (HHO) algorithm as the solving algorithm. Finally, the application of this method is demonstrated through the monitoring of carbon emissions in the manufacturing process of a five-axis machine tool (EOC), as a case study. The results validate the method's rapid responsiveness to abnormal carbon emissions, providing alerts. This further confirms the efficacy and feasibility of the proposed approach. Ultimately, this approach offers a viable strategy for fostering environmentally conscious and high-quality growth in the machine tool industry.