The Metrological Traceability, Performance and Precision of European Radon Calibration Facilities

An interlaboratory comparison for European radon calibration facilities was conducted to evaluate the establishment of a harmonized quality level for the activity concentration of radon in air and to demonstrate the performance of the facilities when calibrating measurement instruments for radon. Fifteen calibration facilities from 13 different European countries participated. They represented different levels in the metrological hierarchy: national metrology institutes and designated institutes, national authorities for radiation protection and participants from universities. The interlaboratory comparison was conducted by the German Federal Office for Radiation Protection (BfS) and took place from 2018 to 2020. Participants were requested to measure radon in atmospheres of their own facilities according to their own procedures and requirements for metrological traceability. A measurement device with suitable properties was used to determine the comparison values. The results of the comparison showed that the radon activity concentrations that were determined by European calibration facilities complying with metrological traceability requirements were consistent with each other and had common mean values. The deviations from these values were normally distributed. The range of variation of the common mean value was a measure of the degree of agreement between the participants. For exposures above 1000 Bq/m3, the variation was about 4% for a level of confidence of approximately 95% (k=2). For lower exposure levels, the variation increased to about 6%.

A Meta-Analysis for Simultaneously Estimating Individual Means with Shrinkage, Isotonic Regression and Pretests

Meta-analyses combine the estimators of individual means to estimate the common mean of a population. However, the common mean could be undefined or uninformative in some scenarios where individual means are “ordered” or “sparse”. Hence, assessments of individual means become relevant, rather than the common mean. In this article, we propose simultaneous estimation of individual means using the James–Stein shrinkage estimators, which improve upon individual studies’ estimators. We also propose isotonic regression estimators for ordered means, and pretest estimators for sparse means. We provide theoretical explanations and simulation results demonstrating the superiority of the proposed estimators over the individual studies’ estimators. The proposed methods are illustrated by two datasets: one comes from gastric cancer patients and the other from COVID-19 patients.

The Statistical Power and Confidence of Some Key Comparison Analysis Methods to Correctly Identify Participant Bias

The validity of calibration and measurement capability (CMC) claims by national metrology institutes is supported by the results of international measurement comparisons. Many methods of comparison analysis are described in the literature and some have been recommended by CIPM Consultative Committees. However, the power of various methods to correctly identify biased results is not well understood. In this work, the statistical power and confidence of some methods of interest to the CIPM Consultative Committees were assessed using synthetic data sets with known properties. Our results show that the common mean model with largest consistent subset delivers the highest statistical power under conditions likely to prevail in mature technical fields, where most participants are in agreement and CMC claims can reasonably be supported by the results of the comparison. Our approach to testing methods is easily applicable to other comparison scenarios or analysis methods and will help the metrology community to choose appropriate analysis methods for comparisons in mature technical fields.

Central data monitoring in the multicentre randomised SafeBoosC-III trial – a pragmatic approach

Background Data monitoring of clinical trials is a tool aimed at reducing the risks of random errors (e.g. clerical errors) and systematic errors, which include misinterpretation, misunderstandings, and fabrication. Traditional ‘good clinical practice data monitoring’ with on-site monitors increases trial costs and is time consuming for the local investigators. This paper aims to outline our approach of time-effective central data monitoring for the SafeBoosC-III multicentre randomised clinical trial and present the results from the first three central data monitoring meetings. Methods The present approach to central data monitoring was implemented for the SafeBoosC-III trial, a large, pragmatic, multicentre, randomised clinical trial evaluating the benefits and harms of treatment based on cerebral oxygenation monitoring in preterm infants during the first days of life versus monitoring and treatment as usual. We aimed to optimise completeness and quality and to minimise deviations, thereby limiting random and systematic errors. We designed an automated report which was blinded to group allocation, to ease the work of data monitoring. The central data monitoring group first reviewed the data using summary plots only, and thereafter included the results of the multivariate Mahalanobis distance of each centre from the common mean. The decisions of the group were manually added to the reports for dissemination, information, correcting errors, preventing furture errors and documentation. Results The first three central monitoring meetings identified 156 entries of interest, decided upon contacting the local investigators for 146 of these, which resulted in correction of 53 entries. Multiple systematic errors and protocol violations were identified, one of these included 103/818 randomised participants. Accordingly, the electronic participant record form (ePRF) was improved to reduce ambiguity. Discussion We present a methodology for central data monitoring to optimise quality control and quality development. The initial results included identification of random errors in data entries leading to correction of the ePRF, systematic protocol violations, and potential protocol adherence issues. Central data monitoring may optimise concurrent data completeness and may help timely detection of data deviations due to misunderstandings or fabricated data.

Stochastic Single Machine JIT Scheduling with Geometric Processing Times and Due Dates

In this paper, we investigate a static stochastic single machine JIT scheduling problem in which the jobs’ processing times are stochastically independent and follow geometric distributions whose mean is provided, due dates are geometrically distributed with a common mean, and both the unit penalty of earliness/tardiness and the fixed penalty of earliness/tardiness are deterministic and different. The objective is to minimize the expected total penalties for quadratic earliness, quadratic tardiness, and early and tardy jobs. We prove that the optimal schedule to minimize this problem is V-shaped with respect to the ratio of mean processing time to unit tardiness penalty under the specific condition. Also, we show a special case and two theorems related to this JIT scheduling problem under specific situations where the optimal solutions exist. Finally, based on the V-shaped characteristic, a dynamic programming algorithm is designed to achieve an optimal V-shaped schedule in pseudopolynomial time.

Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

Suicide deaths among medical students, residents and physicians in India spanning a decade (2010–2019): An exploratory study using on line news portals and Google database

Background: Despite having one of the world’s largest medical education consortium, India lacks a comprehensive and nationally representative data on suicide deaths among medical students and physicians unlike the one found in most of the developed nations of the world. Aim: We aimed to explore the different characteristics of suicide deaths among medical students, residents and physicians in India over a decade (2010–2019). Methods: Content analysis of all suicide death reports among medical students, residents and physicians available from online news portals and other publicly available sites was done. Search was done retrospectively using pertinent search words individually or in combination with language restricted to Hindi and English and timed from January 2010 to December 2019. Reports on completed suicide by allopathic medical students, residents and physicians from India were included. Socio-demographic and suicidological variables were analysed using R software. Results: A total of 358 suicide deaths among medical students (125), residents (105) and physicians (128) were reported between 2010 and 2019. Around 7 out of 10 suicides happened before the age of 30 and had mean age 29.9 (±12.2) years. Female residents and physicians were younger than their male counterparts at the time of suicide. Overall maximum suicide deaths were concentrated in South India except the state of Kerala. The specialty of anesthesiology (22.4%) followed by obstetrics-gynaecology (16.0%) had the highest suicide deaths. Violent suicide methods were more commonly used by all, with hanging being the most common mean of suicide. Academic stress among medical students (45.2%) and residents (23.1%), and marital discord among physicians (26.7%) were the most noticeable reasons for suicide. Mental health problems were the next most common reason in medical students (24%) and physicians (20%) while harassment (20.5%) was in residents. Twenty six percent had exhibited suicide warning signs and only 13% had ever sought psychiatric help before ending their lives. A total of nine reports of suicide pact were found with the average deaths per pact being 2.4 and predominantly driven by financial reasons. Conclusion: Academic stress among medical students and residents, and marital discord in physicians emerged as the key reasons for suicide. However, this preventable domain should be further explored through focused research. This is the first of its kind study from India which attempted to explore this vital yet neglected public health issue using the most feasible and practical method of online news content-based analysis.

Adherence and persistence of optimal medical therapy after myocardial infarction in a low to middle income country

Funding Acknowledgements Type of funding sources: None. Background Adherence to medical therapy after myocardial infarction (MI) is a crucial part of patient care and indispensable for reaching clinical goals, however, data from low to middle income countries (LMIC) regarding adherence and persistence of optimal medical treatment (OMT) is scarce. Purpose To evaluate adherence and persistence to OMT after acute coronary syndrome (ACS) in a cohort of patients with ST elevation myocardial infarction (STEMI) in a low to middle income country. Methods We conducted a survey study evaluating adherence and persistence of OMT after 6 months of the index event in patients with STEMI. Patients were surveyed via phone call using the simplified medication adherence questionnaire (SMAQ) tool, which has been previously validated (both in English and Spanish) as a clinical tool to evaluate adherence to medication. We evaluated persistence of OMT as well. A secure electronic database was constructed to capture information, regarding adherence and persistence, and other clinically relevant variables. Study population The study included consecutive patients aged 18-99 years old with the diagnosis of STEMI form Mexico City’s STEMI Network, who received either pharmacoinvasive strategy (PIS) or Primary Percutaneous Coronary Intervention (pPCI) during the first 12 hours from symptom onset. This population is derived from the PHASE-Mx study (ClinicalTrials.gov Identifier: NCT03974581), which results have been previously published. Results A total of 602 patients were initially screened; among these, 158 patients (26.2%) were lost to contact, 5 patients (n = 0.008%) refused to answer and 65 patients (10.7%) died during follow up. The final analytic sample consisted of 375 patients; among them, 192 (51.2%) received primary PCI and 183 (48.8%) received pharmacoinvasive strategy. Mean age was 58 + 10 years old and most of the patients were male (90.1%). Hypertension (44.8%) and diabetes (32.0%) were common. Mean follow-up time after index STEMI was 650 (IQR: 416-832) days. After SMAQ evaluation, only 26.1% of the patients were considered to be adherent to their medications (>95% compliance), as shown in the Table 1. Persistence of OMT after STEMI included: ASA (84.6%), P2Y12i (71.5%), statin (83.6%), ACEI/ARB (77.1%) and beta blocker (63.7%) (Table 2). Conclusions In patients with STEMI in a low to middle income country, persistence and adherence to OMT were low. Actions to improve adherence to therapy after mayor cardiovascular events are needed. Risk factors associated to poor adherence included diabetes (OR 0.46), age (OR 0.76) and atrial fibrillation (OR 0.42). Abstract Figure.