Differentiating poor validity from probable impairment on the medical symptom validity test: a cross-validation study

Aims: In neuropsychological evaluations, it is often difficult to ascertain whether poor performance on measures of validity is due to poor effort or malingering, or whether there is genuine cognitive impairment. Dunham and Denney created an algorithm to assess this question using the Medical Symptom Validity Test (MSVT). We assessed the ability of their algorithm to detect poor validity versus probable impairment, and concordance of failure on the MSVT with other freestanding tests of performance validity.

Methods: Two previously published datasets (n?=?153 and n?=?641, respectively) from outpatient neuropsychological evaluations were used to test Dunham and Denney’s algorithm, and to assess concordance of failure rates with the Test of Memory Malingering and the forced choice measure of the California Verbal Learning Test, two commonly used performance validity tests.

Results: In both datasets, none of the four cutoff scores for failure on the MSVT (70%, 75%, 80%, or 85%) identified a poor validity group with proportionally aligned failure rates on other freestanding measures of performance validity. Additionally, the protocols with probable impairment did not differ from those with poor validity on cognitive measures.

Conclusions: Despite what appeared to be a promising approach to evaluating failure on the easy MSVT subtests when clinical data are unavailable (as recommended in the advanced interpretation program, or advanced interpretation [AI], of the MSVT), the current findings indicate the AI remains the gold standard for doing so. Future research should build on this effort to address shortcomings in measures of effort in neuropsychological evaluations.     

¿Es el intervalo libre de docetaxel un factor predictivo para el cáncer de próstata resistente a la castración?

ObjectiveProstate cancer (PCa) is the second most common solid tumor in men and the fifth leading cause of cancer-related death. In advanced stage, palliative treatments are used instead of curative therapies. Therefore, finding predictive indicators seems crucial. Patients with castration-resistant prostate cancer (CRPC) that received Dx chemotherapy have been retrospectively reviewed. The aim of this study was to investigate whether docetaxel (Dx)-free interval could have a predictive value for PCa and influence other sequential therapies.Material and methodsThis clinical trial study was performed on 104 patients at Medeniyet University Oncology Clinic in 2018-2020. All CRPC patients had metastases, received Dx as first-line treatment and underwent androgen receptor axis targeted (ARAT) therapy after disease progression. We analyzed patients’ progression time after Dx therapy and the effects on sequential treatment.ResultsAfter Dx therapy, all patients received ARAT (abiraterone (ABI) n: 49 (47.1%) and enzalutamide (ENZ) n: 54 (51.9%)) as a second-line treatment, except for one patient who received cabazitaxel. There was a statistically significant relationship between the Dx-free interval and duration of response to ARAT (P<.001). The response time of ARAT treatment was <10.5 months in all patients whose Dx-free interval period was <9 months.ConclusionsOur findings support the theory that Dx-free interval can be a predictive factor for CRPC. CRPC disease can be classified as Dx-sensitive disease or Dx-resistance disease, based on the Dx-free interval. Decision on subsequent treatments could be made considering this information.     

Troubled-Cell Indicator Based on Mean Value Property for Hybrid WENO Schemes

In this paper, we introduce a new type of troubled-cell indicator to improve hybrid weighted essentially non-oscillatory (WENO) schemes for solving the hyperbolic conservation laws. The hybrid WENO schemes selectively adopt the high-order linear upwind scheme or the WENO scheme to avoid the local characteristic decompositions and calculations of the nonlinear weights in smooth regions. Therefore, they can reduce computational cost while maintaining non-oscillatory properties in non-smooth regions. Reliable troubled-cell indicators are essential for efficient hybrid WENO methods. Most of troubled-cell indicators require proper parameters to detect discontinuities precisely, but it is very difficult to determine the parameters automatically. We develop a new troubled-cell indicator derived from the mean value theorem that does not require any variable parameters. Additionally, we investigate the characteristics of indicator variable; one of the conserved properties or the entropy is considered as indicator variable. Detailed numerical tests for 1D and 2D Euler equations are conducted to demonstrate the performance of the proposed indicator. The results with the proposed troubled-cell indicator are in good agreement with pure WENO schemes. Also the new indicator has advantages in the computational cost compared with the other indicators.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

薄层色谱法在当前中药质量标准中的应用探讨

薄层色谱鉴别在历版《中国药典》中的应用经历了从无到有、从少到多的过程；而薄层扫描含量测定在最近几版《中国药典》中的应用比例逐渐降低。随着对中药质量标准体系要求的进一步提高，薄层色谱法的不足之处陆续显现，如仪器普及率低、设备并不简单、结果重复性和稳定性较差、鉴别速度及准确性不及高效液相色谱法、展开剂毒性大等，逐渐不合时宜。在制定中药质量标准时，研究者不应该墨守成规，薄层色谱鉴别也不应该是雷打不动的定性鉴别必备选项。高效液相色谱法具备完全取代薄层色谱法的可行性，薄层色谱法可作为高效液相色谱法的补充。为充分降低检测成本、缩短检测周期、提高鉴别效率，笔者建议中药质量标准体系应该大幅减少薄层色谱鉴别方法的应用，增加高效液相特征图谱鉴定，尽量做到“一个条件，一张图谱”；除非确有必要，《中国药典》等国家质量标准体系应将薄层色谱鉴别作为推荐方法，而非强制标准。     

Care of Seniors with Breast Cancer – Treatment Received and Refining Decision Making

AimsTreatment decisions for older patients with breast cancer are complex and evidence is largely extrapolated from younger populations. Frailty and comorbidity need to be considered. We studied the baseline characteristics and treatment decisions in older patients in Christchurch with breast cancer and assessed survival outcomes and prognostic/discriminatory performance of several tools.Materials and methodsWe searched the Canterbury Breast Cancer Registry and identified patients aged 70 years or older at diagnosis with invasive, non-metastatic breast cancer between 1 June 2009 and 30 June 2015. We retrieved demographics, treatment and outcome information. Overall survival and breast cancer-specific survival were estimated. Tools analysing performance status and comorbidity were assessed for their prognostic and discriminatory power.ResultsIn total, 440 patients were identified. Primary surgery was carried out for 362 patients (82.3%): breast-conserving surgery in 114 (of whom 88.6% received radiation therapy); mastectomy in 248 (of whom 24.6% received radiation). Hormone therapy was given for 265 (71.1%) patients with oestrogen receptor-positive cancers. Two hundred and seventy-four (62.3%) patients received full standard treatment, which was associated with significantly improved 5-year survival and 5-year breast cancer-specific survival. The median estimated overall survival was 8.2 years (95% confidence interval 7.3–9.1 years). Of those who died, 71.3% of deaths were due to causes other than breast cancer or unknown causes. The comorbidity-adjusted life expectancy (CALE) showed partial prognostic accuracy. CALE, Charlson and Eastern Cooperative Oncology Group tools all showed discriminatory value.ConclusionIn this population-based series of older patients with breast cancer, showing high levels of primary and adjuvant treatment, patients were more likely to die of causes other than breast cancer. Performance status and comorbidity tools showed prognostic and discriminatory potential in this population supporting their use in treatment decision making. CALE showed the most potential to improve treatment decisions but requires validation in this population to improve prognostic accuracy.