Target trial emulation using new comorbidity indices provided risk estimates comparable to a randomized trial.

OBJECTIVES: To quantify the ability of two new comorbidity indices to adjust for confounding, by benchmarking a target trial emulation against the randomized controlled trial (RCT) result. STUDY DESIGN AND SETTING: Observational study including 18,316 men from Prostate Cancer data Base Sweden 5.0, diagnosed with prostate cancer between 2008 and 2019 and treated with primary radical prostatectomy (RP, n = 14,379) or radiotherapy (RT, n = 3,937). The effect on adjusted risk of death from any cause after adjustment for comorbidity by use of two new comorbidity indices, the multidimensional diagnosis-based comorbidity index and the drug comorbidity index, were compared to adjustment for the Charlson comorbidity index (CCI). RESULTS: Risk of death was higher after RT than RP (hazard ratio [HR] = 1.94; 95% confidence interval [CI]: 1.70-2.21). The difference decreased when adjusting for age, cancer characteristics, and CCI (HR = 1.32, 95% CI: 1.06-1.66). Adjustment for the two new comorbidity indices further attenuated the difference (HR 1.14, 95% CI 0.91-1.44). Emulation of a hypothetical pragmatic trial where also older men with any type of baseline comorbidity were included, largely confirmed these results (HR 1.10; 95% CI 0.95-1.26). CONCLUSION: Adjustment for comorbidity using two new indices provided comparable risk of death from any cause in line with results of a RCT. Similar results were seen in a broader study population, more representative of clinical practice.

Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation.

PURPOSE: The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived segmentations are poorly understood; our study aims to quantify these factors. METHODS: Organ at risk (OAR) and tumor-related segmentations provided by radiation oncologists from the Contouring Collaborative for Consensus in Radiation Oncology data set were used. Segmentations were derived from five disease sites: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and GI. Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus, which served as a reference standard benchmark. The Dice similarity coefficient (DSC) was primarily used as a metric for the comparisons. DSC was stratified into binary groups on the basis of structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Bayesian estimation were used to investigate the association between demographic variables and the binarized DSC for each disease site. Variables with a highest density interval excluding zero were considered to substantially affect the outcome measure. RESULTS: Five hundred seventy-four, 110, 452, 112, and 48 segmentations were used for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of segmentations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumors, respectively. Regression analysis revealed that the structure being tumor-related had a substantial negative impact on binarized DSC for the breast, sarcoma, H&N, and GI cases. There were no recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations. CONCLUSION: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks.

Leveraging the potential of the German operating room benchmarking initiative for planning: A ready-to-use surgical process data set.

We present a freely available data set of surgical case mixes and surgery process duration distributions based on processed data from the German Operating Room Benchmarking initiative. This initiative collects surgical process data from over 320 German, Austrian, and Swiss hospitals. The data exhibits high levels of quantity, quality, standardization, and multi-dimensionality, making it especially valuable for operating room planning in Operations Research. We consider detailed steps of the perioperative process and group the data with respect to the hospital's level of care, the surgery specialty, and the type of surgery patient. We compare case mixes for different subgroups and conclude that they differ significantly, demonstrating that it is necessary to test operating room planning methods in different settings, e.g., using data sets like ours. Further, we discuss limitations and future research directions. Finally, we encourage the extension and foundation of new operating room benchmarking initiatives and their usage for operating room planning.

Benchmarking multi-ancestry prostate cancer polygenic risk scores in a real-world cohort.

Prostate cancer is a heritable disease with ancestry-biased incidence and mortality. Polygenic risk scores (PRSs) offer promising advancements in predicting disease risk, including prostate cancer. While their accuracy continues to improve, research aimed at enhancing their effectiveness within African and Asian populations remains key for equitable use. Recent algorithmic developments for PRS derivation have resulted in improved pan-ancestral risk prediction for several diseases. In this study, we benchmark the predictive power of six widely used PRS derivation algorithms, including four of which adjust for ancestry, against prostate cancer cases and controls from the UK Biobank and All of Us cohorts. We find modest improvement in discriminatory ability when compared with a simple method that prioritizes variants, clumping, and published polygenic risk scores. Our findings underscore the importance of improving upon risk prediction algorithms and the sampling of diverse cohorts.

NKUT: Dataset and Benchmark for Pediatric Mandibular Wisdom Teeth Segmentation.

Germectomy is a common surgery in pediatric dentistry to prevent the potential dangers caused by impacted mandibular wisdom teeth. Segmentation of mandibular wisdom teeth is a crucial step in surgery planning. However, manually segmenting teeth and bones from 3D volumes is time-consuming and may cause delays in treatment. Deep learning based medical image segmentation methods have demonstrated the potential to reduce the burden of manual annotations, but they still require a lot of well-annotated data for training. In this paper, we initially curated a Cone Beam Computed Tomography (CBCT) dataset, NKUT, for the segmentation of pediatric mandibular wisdom teeth. This marks the first publicly available dataset in this domain. Second, we propose a semantic separation scale-specific feature fusion network named WTNet, which introduces two branches to address the teeth and bones segmentation tasks. In WTNet, We design a Input Enhancement (IE) block and a Teeth-Bones Feature Separation (TBFS) block to solve the feature confusions and semantic-blur problems in our task. Experimental results suggest that WTNet performs better on NKUT compared to previous state-of-the-art segmentation methods (such as TransUnet), with a maximum DSC lead of nearly 16%.

European value-based healthcare benchmarking: moving from theory to practice.

BACKGROUND: Value-based healthcare (VBHC) is a conceptual framework to improve the value of healthcare by health, care-process and economic outcomes. Benchmarking should provide useful information to identify best practices and therefore a good instrument to improve quality across healthcare organizations. This paper aims to provide a proof-of-concept of the feasibility of an international VBHC benchmarking in breast cancer, with the ultimate aim of being used to share best practices with a data-driven approach among healthcare organizations from different health systems. METHODS: In the VOICE community-a European healthcare centre cluster intending to address VBHC from theory to practice-information on patient-reported, clinical-related, care-process-related and economic-related outcomes were collected. Patient archetypes were identified using clustering techniques and an indicator set following a modified Delphi was defined. Benchmarking was performed using regression models controlling for patient archetypes and socio-demographic characteristics. RESULTS: Six hundred and ninety patients from six healthcare centres were included. A set of 50 health, care-process and economic indicators was distilled for benchmarking. Statistically significant differences across sites have been found in most health outcomes, half of the care-process indicators, and all economic indicators, allowing for identifying the best and worst performers. CONCLUSIONS: To the best of our knowledge, this is the first international experience providing evidence to be used with VBHC benchmarking intention. Differences in indicators across healthcare centres should be used to identify best practices and improve healthcare quality following further research. Applied methods might help to move forward with VBHC benchmarking in other medical conditions.

Benchmarking long-read RNA-sequencing analysis tools using in silico mixtures.

The lack of benchmark data sets with inbuilt ground-truth makes it challenging to compare the performance of existing long-read isoform detection and differential expression analysis workflows. Here, we present a benchmark experiment using two human lung adenocarcinoma cell lines that were each profiled in triplicate together with synthetic, spliced, spike-in RNAs (sequins). Samples were deeply sequenced on both Illumina short-read and Oxford Nanopore Technologies long-read platforms. Alongside the ground-truth available via the sequins, we created in silico mixture samples to allow performance assessment in the absence of true positives or true negatives. Our results show that StringTie2 and bambu outperformed other tools from the six isoform detection tools tested, DESeq2, edgeR and limma-voom were best among the five differential transcript expression tools tested and there was no clear front-runner for performing differential transcript usage analysis between the five tools compared, which suggests further methods development is needed for this application.

Considerations for application of benchmark dose modeling in radiation research: workshop highlights.

BACKGROUND: Exposure to different forms of ionizing radiation occurs in diverse occupational, medical, and environmental settings. Improving the accuracy of the estimated health risks associated with exposure is therefore, essential for protecting the public, particularly as it relates to chronic low dose exposures. A key aspect to understanding health risks is precise and accurate modeling of the dose-response relationship. Toward this vision, benchmark dose (BMD) modeling may be a suitable approach for consideration in the radiation field. BMD modeling is already extensively used for chemical hazard assessments and is considered statistically preferable to identifying low and no observed adverse effects levels. BMD modeling involves fitting mathematical models to dose-response data for a relevant biological endpoint and identifying a point of departure (the BMD, or its lower bound). Recent examples in chemical toxicology show that when applied to molecular endpoints (e.g. genotoxic and transcriptional endpoints), BMDs correlate to points of departure for more apical endpoints such as phenotypic changes (e.g. adverse effects) of interest to regulatory decisions. This use of BMD modeling may be valuable to explore in the radiation field, specifically in combination with adverse outcome pathways, and may facilitate better interpretation of relevant in vivo and in vitro dose-response data. To advance this application, a workshop was organized on June 3rd, 2022, in Ottawa, Ontario that brought together BMD experts in chemical toxicology and the radiation scientific community of researchers, regulators, and policy-makers. The workshop's objective was to introduce radiation scientists to BMD modeling and its practical application using case examples from the chemical toxicity field and demonstrate the BMDExpress software using a radiation dataset. Discussions focused on the BMD approach, the importance of experimental design, regulatory applications, its use in supporting the development of adverse outcome pathways, and specific radiation-relevant examples. CONCLUSIONS: Although further deliberations are needed to advance the use of BMD modeling in the radiation field, these initial discussions and partnerships highlight some key steps to guide future undertakings related to new experimental work.

Systematic benchmarking of statistical methods to assess differential expression of circular RNAs.

Circular RNAs (circRNAs) are covalently closed transcripts involved in critical regulatory axes, cancer pathways and disease mechanisms. CircRNA expression measured with RNA-seq has particular characteristics that might hamper the performance of standard biostatistical differential expression assessment methods (DEMs). We compared 38 DEM pipelines configured to fit circRNA expression data's statistical properties, including bulk RNA-seq, single-cell RNA-seq (scRNA-seq) and metagenomics DEMs. The DEMs performed poorly on data sets of typical size. Widely used DEMs, such as DESeq2, edgeR and Limma-Voom, gave scarce results, unreliable predictions or even contravened the expected behaviour with some parameter configurations. Limma-Voom achieved the most consistent performance throughout different benchmark data sets and, as well as SAMseq, reasonably balanced false discovery rate (FDR) and recall rate. Interestingly, a few scRNA-seq DEMs obtained results comparable with the best-performing bulk RNA-seq tools. Almost all DEMs' performance improved when increasing the number of replicates. CircRNA expression studies require careful design, choice of DEM and DEM configuration. This analysis can guide scientists in selecting the appropriate tools to investigate circRNA differential expression with RNA-seq experiments.

Evaluation of a beach volleyball skill instrument for the line shot attack / Avaliação de um instrumento de habilidade de voleibol de praia para o ataque de tiro à linha

ABSTRACT This study aimed to develop a test for measuring athletes' performance. Logical, content, construct, criterion validity, as well as reliability, were examined. Three beach volleyball experts evaluated how well the test evaluates the shot skill accuracy according to their judgment (logical validity). A group of eight beach volleyball experts analyzed the test and rated it on a scale of 1 to 7 regarding the relevance of the content as a measure of beach volleyball athlete's accuracy, the average score was calculated at 6.75 (SD=0.46) (content validity). 60 beach volleyball players divided into three equal groups of novices, intermediates, and experts were asked to undergo the test on two separate days. A one-way ANOVA between the three groups was performed, which indicated that the mean scores of the three groups differed significantly (p<0.001 for both days): experts achieved better scores than intermediates who in turn scored better than novices (construct validity). The athletes' scores were correlated with their proficiency level, using the Pearson Correlation test, which indicated very high positive (Day-1: r=0.956, n=60, p<0.001; Day-2: r=0.953, n=60, p<0.001) (criterion validity). The intraclass correlation coefficient was calculated between Day-1 and Day-2 and was found equal to 0.980 for average measures (p<0.001) and 0.961 for single measures (p<0.001). In conclusion, the requirements for logical, content, construct, criterion validity, and reliability have been met. Therefore, the line shot attack test can be used as a valid and reliable measurement instrument for evaluating the performance of beach volleyball athletes.

RESUMO Este estudo teve como objetivo desenvolver um teste para mensurar o desempenho de atletas. No teste foram examinados os seguintes itens: lógica, conteúdo, construto, validade de critério, bem como confiabilidade. Três especialistas em voleibol de praia avaliaram o quão bem o teste avalia a precisão da habilidade de arremesso de acordo com seu julgamento (validade lógica). Um grupo de oito especialistas em vôlei de praia analisou o teste e o classificou em uma escala de 1 a 7 quanto à relevância do conteúdo como medida de precisão do atleta de vôlei de praia, a pontuação média foi calculada em 6,75 (DP=0,46) (validade de conteúdo). A amostra foi constituída por 60 jogadores de vôlei de praia divididos em três grupos: novatos, intermediários e experientes foram convidados a realizar o teste em dois dias separados. Uma ANOVA de uma via entre os três grupos foi realizada, o que indicou que as pontuações médias dos três grupos diferiram significativamente (p<0,001 para ambos os dias): os especialistas obtiveram melhores pontuações do que os intermediários que, por sua vez, pontuaram melhor do que os novatos (validade de construção). As pontuações dos atletas foram correlacionadas com seu nível de proficiência, usando o teste de correlação de Pearson, indicando correlação muito alta e positiva (Dia-1: r=0,956, n=60, p<0,001; Dia-2: r=0,953, n=60, p<0,001) (validade de critério). O coeficiente de correlação intraclasse foi calculado entre o Dia-1 e o Dia-2 e foi igual a 0,980 para medidas médias (p<0,001) e 0,961 para medidas isoladas (p<0,001). Em conclusão, os requisitos de lógica, conteúdo, construto, validade de critério e confiabilidade foram atendidos. Portanto, o teste de ataque de tiro de linha pode ser usado como um instrumento de medida válido e confiável para avaliar o desempenho de atletas de vôlei de praia.

Facts and Fallacy of Benchmark Performance Indicators.

Efforts to improve quality in healthcare have arisen from the recognition that the quality of care delivered and resulting outcomes are highly variable. Performance benchmarking using high-quality data to compare risk-adjusted outcomes between hospitals and surgeons has been widely adopted as one means for addressing this problem. In this article we discuss the history, current state, methodologies, and potential pitfalls of benchmarking efforts to improve quality of healthcare in the United States.

Comparison of the lower limit of benchmark dose confidence interval with no-observed-adverse-effect level by applying four different software for tumorigenicity testing of pesticides in Japan.

The benchmark dose (BMD) approach is updated to create an international harmonizing process following rapid theoretical sophistication. We calculated the lower limit of BMD confidence interval (BMDL) for carcinogenicity based on 193 tumorigenicity bioassay data published in 50 pesticide risk assessment reports by the Food Safety Commission of Japan (FSCJ) to validate the appropriateness and necessity for the refinement of the FSCJ-established BMD guidance. Three well-known BMD software, PROAST, BMDS, and BBMD were used to compare their BMDLs with no-observed-adverse-effect levels (NOAELs) for carcinogenicity. Recently implemented methodologies such as model averaging or Bayesian inference were also used. Our results indicate that the BMD approach provides a point of departure similar to the NOAEL approach if the data used exhibit a clear dose-response relationship. In some cases, particularly in software with a frequentist approach, the calculation failed to provide BMDL or provided considerably lower BMDLs than NOAELs. However, most of the datasets that resulted in failed calculations or extremely low BMDLs exhibited unclear dose-response relationships, i.e., non-monotonous and sporadic responses. The expert review on the shape of the dose-response plot would help better apply the BMD approach. Furthermore, we observed that Bayesian approaches provided fewer failed or extreme BMD calculations than the frequentist approaches.

Aprendizaje automático aplicado en área de la salud: parte 2 / Machine Learning In Healthcare: part 2

En el artículo anterior se introdujo el tema y se desarrolló cómo es la recolección y análisis de datos, la selección y entrenamiento de modelos de aprendizaje automático supervisados y los métodos de validación interna que permiten corroborar si el modelo arroja resultados similares a los de otros conjuntos de entrenamiento y de prueba. En este artículo continuaremos con la descripción de la evaluación del rendimiento, la selección del modelo más adecuado para identificar la característica que se va a evaluar y la validación externa del modelo. Además, el artículo resume los desafíos existentes en la implementación del Machine Learning desde la investigación al uso clínico. (AU)

In the previous article, we introduced topics such as data collection and analysis, selection and training of supervised machine learning models and methods of internal validation that allow to corroborate whether the model yields similar results to other training and test sets.In this article, we will continue with the description of the performance evaluation, selecting the most appropriate model to identify the characteristic to evaluate and the external validation of the model. In addition, the article summarizes the actual challenges in the implementation of machine learning from research to clinical use. (AU)

Deep learning model for fully automated breast cancer detection system from thermograms.

Breast cancer is one of the most common diseases among women worldwide. It is considered one of the leading causes of death among women. Therefore, early detection is necessary to save lives. Thermography imaging is an effective diagnostic technique which is used for breast cancer detection with the help of infrared technology. In this paper, we propose a fully automatic breast cancer detection system. First, U-Net network is used to automatically extract and isolate the breast area from the rest of the body which behaves as noise during the breast cancer detection model. Second, we propose a two-class deep learning model, which is trained from scratch for the classification of normal and abnormal breast tissues from thermal images. Also, it is used to extract more characteristics from the dataset that is helpful in training the network and improve the efficiency of the classification process. The proposed system is evaluated using real data (A benchmark, database (DMR-IR)) and achieved accuracy = 99.33%, sensitivity = 100% and specificity = 98.67%. The proposed system is expected to be a helpful tool for physicians in clinical use.

A multicentre outcome analysis to define global benchmarks for donation after circulatory death liver transplantation.

BACKGROUND & AIMS: The concept of benchmarking is established in the field of transplant surgery; however, benchmark values for donation after circulatory death (DCD) liver transplantation are not available. Thus, we aimed to identify the best possible outcomes in DCD liver transplantation and to propose outcome reference values. METHODS: Based on 2,219 controlled DCD liver transplantations, collected from 17 centres in North America and Europe, we identified 1,012 low-risk, primary, adult liver transplantations with a laboratory MELD score of ≤20 points, receiving a DCD liver with a total donor warm ischemia time of ≤30 minutes and asystolic donor warm ischemia time of ≤15 minutes. Clinically relevant outcomes were selected and complications were reported according to the Clavien-Dindo-Grading and the comprehensive complication index (CCI). Corresponding benchmark cut-offs were based on median values of each centre, where the 75th-percentile was considered. RESULTS: Benchmark cases represented between 19.7% and 75% of DCD transplantations in participating centres. The 1-year retransplant and mortality rates were 4.5% and 8.4% in the benchmark group, respectively. Within the first year of follow-up, 51.1% of recipients developed at least 1 major complication (≥Clavien-Dindo-Grade III). Benchmark cut-offs were ≤3 days and ≤16 days for ICU and hospital stay, ≤66% for severe recipient complications (≥Grade III), ≤16.8% for ischemic cholangiopathy, and ≤38.9 CCI points 1 year after transplant. Comparisons with higher risk groups showed more complications and impaired graft survival outside the benchmark cut-offs. Organ perfusion techniques reduced the complications to values below benchmark cut-offs, despite higher graft risk. CONCLUSIONS: Despite excellent 1-year survival, morbidity in benchmark cases remains high. Benchmark cut-offs targeting morbidity parameters offer a valid tool to assess the protective value of new preservation technologies in higher risk groups and to provide a valid comparator cohort for future clinical trials. LAY SUMMARY: The best possible outcomes after liver transplantation of grafts donated after circulatory death (DCD) were defined using the concept of benchmarking. These were based on 2,219 liver transplantations following controlled DCD donation in 17 centres worldwide. Donor and recipient combinations with higher risk had significantly worse outcomes. However, the use of novel organ perfusion technology helped high-risk patients achieve similar outcomes as the benchmark cohort.

A comparative physicochemical and pharmacological evaluation of dexamethasone sodium phosphate and betamethasone sodium phosphate mucoadhesive gels for the treatment of oral submucous fibrosis in rats

Abstract The present study is aimed to formulate steroidal oral mucoadhesive gels of dexamethasone sodium phosphate and betamethasone sodium phosphate. Six gel formulations each of dexamethasone sodium phosphate and betamethasone sodium phosphate prepared using two different polymers carboxymethyl cellulose sodium and hydroxypropyl methylcellulose, in variable proportions. All the formulations subjected for assessment of various physicochemical parameters and mechanical properties. The formulations BSP5 and DSP5, both containing 1.25 % carboxymethyl cellulose sodium, 1.25 % hydroxypropyl methylcellulose, exhibiting mucoadhesive strength of 12.300 ± 0.004 and 12.600 ± 0.01, adhesiveness of 28.04 ± 00 and 30.02 ± 00, cohesiveness of 28.04 ± 00 and 30.02 ± 00, drug release of 86.869 ± 0.380 % and 88.473 ± 0.457 % respectively were considered as promising ones and were further subjected for stability studies and in vivo study in male albino rats. Formulation DSP5 upon oral application for 4 months in arecoline induced oral submucous fibrosis rats, showed more than 80 % reduction in fibrosis as compared with BSP5 which showed nearly 50 % reduction. These results were concluded on the basis of histopathological profile and weight gain among the experimental animals during in vivo study. Hence, DSP5 by minimizing the painful injuries and morbidities justifies being suitable noninvasive model for OSMF treatment.

Benchmarking pipelines for subclonal deconvolution of bulk tumour sequencing data.

Intratumour heterogeneity provides tumours with the ability to adapt and acquire treatment resistance. The development of more effective and personalised treatments for cancers, therefore, requires accurate characterisation of the clonal architecture of tumours, enabling evolutionary dynamics to be tracked. Many methods exist for achieving this from bulk tumour sequencing data, involving identifying mutations and performing subclonal deconvolution, but there is a lack of systematic benchmarking to inform researchers on which are most accurate, and how dataset characteristics impact performance. To address this, we use the most comprehensive tumour genome simulation tool available for such purposes to create 80 bulk tumour whole exome sequencing datasets of differing depths, tumour complexities, and purities, and use these to benchmark subclonal deconvolution pipelines. We conclude that i) tumour complexity does not impact accuracy, ii) increasing either purity or purity-corrected sequencing depth improves accuracy, and iii) the optimal pipeline consists of Mutect2, FACETS and PyClone-VI. We have made our benchmarking datasets publicly available for future use.

Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer.

Smartphone usage is nearly ubiquitous worldwide, and Android provides the leading open-source operating system, retaining the most significant market share and active user population of all open-source operating systems. Hence, malicious actors target the Android operating system to capitalize on this consumer reliance and vulnerabilities present in the system. Hackers often use confidential user data to exploit users for advertising, extortion, and theft. Notably, most Android malware detection tools depend on conventional machine-learning algorithms; hence, they lose the benefits of metaheuristic optimization. Here, we introduce a novel detection system based on optimizing the random vector functional link (RVFL) using the artificial Jellyfish Search (JS) optimizer following dimensional reduction of Android application features. JS is used to determine the optimal configurations of RVFL to improve classification performance. RVFL+JS minimizes the runtime of the execution of the optimized models with the best performance metrics, based on a dataset consisting of 11,598 multi-class applications and 471 static and dynamic features.

Tumor BRCA testing in ovarian cancer and EQA scheme: our experience of a critical evaluation.

Next generation sequencing (NGS) is a widespread molecular biology method integrated into clinical practice to detect genetic variants, for diagnostic and prognostic purposes. The scheduled external quality assessments (EQA) is integral part of clinical molecular laboratory quality assurance. The EQA provides an efficient system to compare analytic test performances among different laboratories, which is essential to evaluate consistency of molecular test. EQA failures demands targeted corrective action plans. In this context, the complexity of the NGS techniques requires careful and continuous quality control procedures. We report a tumor BRCA1/2 (tBRCA) testing benchmark discrepancy provided by the European Molecular Genetics Quality Network in our laboratory during a round of EQA for somatic mutation testing of BRCA genes in relation to ovarian cancer. The critical analysis emerging from the tBRCA EQA is presented. We underline that harmonization processes are still required for the EQA in the molecular biology field, especially if applied to the evaluation of methods characterized by high complexity.

Implant brand portfolios, the potential for camouflage of data, and the role of the Orthopaedic Data Evaluation Panel in total knee arthroplasty.

AIMS: Knee arthroplasty surgery is a highly effective treatment for arthritis and disorders of the knee. There are a wide variety of implant brands and types of knee arthroplasty available to surgeons. As a result of a number of highly publicized failures, arthroplasty surgery is highly regulated in the UK and many other countries through national registries, introduced to monitor implant performance, surgeons, and hospitals. With time, the options available within many brand portfolios have grown, with alternative tibial or femoral components, tibial insert materials, or shapes and patella resurfacings. In this study we have investigated the effect of the expansion of implant brand portfolios and where there may be a lack of transparency around a brand name. We also aimed to establish the potential numbers of compatible implant construct combinations. METHODS: Hypothetical implant brand portfolios were proposed, and the number of compatible implant construct combinations was calculated. RESULTS: A simple knee portfolio with cemented cruciate-retaining (CR) and posterior-stabilized (PS) components, with and without a patella, has four combinations. If there are two options available for each component, the numbers double for each option, resulting in 32 combinations. The effect of adding a third option multiplies the number by 1.3. Introducing compatible uncemented options, with the effect of hybrids, multiplies the number by 4. An implant portfolio with two femoral components (both in CR and PS), with two insert options and a patella, all in cemented and uncemented versions leads to 192 possible compatible implant construct combinations. There are implant brands available to surgeons with many more than two options. CONCLUSION: This study demonstrates that the addition of multiple variants within a knee brand portfolio leads to a large number (many hundreds) of compatible implant construct combinations. Revision rates of implant combinations are not currently reviewed at this level of granularity, leading to the risk of camouflage of true outcomes. Cite this article: Bone Joint J 2021;103-B(10):1555-1560.