Significant Difference In Prediction Accuracy Research Articles

Accuracy Validation of the New Barrett True Axial Length Formula and the Optimized Lens Factor Using Sum-of-Segment Biometry.

Objectives: This study aims to verify the accuracy of a new calculation formula, Barrett true axial length formula (T-AL), and the optimized lens factor (LF) for predicting postoperative refraction after cataract surgery. Methods: We included 156 Japanese patients who underwent cataract surgery using Clareon monofocal intraocular lenses at our clinic between January 2022 and June 2023. Postoperative spherical equivalent was calculated using subjective refraction values obtained 1 month post-surgery. The LFs were optimized so that the mean prediction error (PE) of each calculation formula was zero (zero optimization). We calculated the mean absolute PE (MAE) to assess accuracy and used a Friedman test for statistical comparisons. The accuracy of T-AL and the optimized LFs was compared with that of the conventional Barrett Universal II formula for ARGOS (AR-B) and OA-2000 (OA-B) with equivalent refractive index. Results: For T-AL, AR-B, and OA-B, the MAEs ± standard deviations were 0.225 ± 0.179, 0.219 ± 0.168, and 0.242 ± 0.206 D, respectively. The Friedman test showed no statistically significant differences among the three groups. The device-optimized LFs were 2.248-2.289 (T-AL), 2.236-2.246 (AR-B), and 2.07-2.08 (OA-B); the corresponding zero-optimized LFs were 2.262-2.287 (T-AL), 2.287-2.303 (AR-B), and 2.160-2.170 (OA-B). Conclusion: There were no significant differences in prediction accuracy among the formulas. However, the accuracy of LF optimization varied by device, with T-AL being closest to the value under zero optimization. This suggests that T-AL is clinically useful for predicting an accurate postoperative refraction without zero optimization.

Genome-wide association study and genomic prediction for resistance to brown planthopper in rice.

The brown planthopper (BPH) is the most destructive insect pest that threatens rice production globally. Developing rice varieties incorporating BPH-resistant genes has proven to be an effective control measure against BPH. In this study, we assessed the resistance of a core collection consisting of 502 rice germplasms by evaluating resistance scores, weight gain rates and honeydew excretions. A total of 117 rice varieties (23.31%) exhibited resistance to BPH. Genome-wide association studies (GWAS) were performed on both the entire panel of 502 rice varieties and its subspecies, and 6 loci were significantly associated with resistance scores (P value < 1.0e-8). Within these loci, we identified eight candidate genes encoding receptor-like protein kinase (RLK), nucleotide-binding and leucine-rich repeat (NB-LRR), or LRR proteins. Two loci had not been detected in previous study and were entirely novel. Furthermore, we evaluated the predictive ability of genomic selection for resistance to BPH. The results revealed that the highest prediction accuracy for BPH resistance reached 0.633. As expected, the prediction accuracy increased progressively with an increasing number of SNPs, and a total of 6.7K SNPs displayed comparable accuracy to 268K SNPs. Among various statistical models tested, the random forest model exhibited superior predictive accuracy. Moreover, increasing the size of training population improved prediction accuracy; however, there was no significant difference in prediction accuracy between a training population size of 737 and 1179. Additionally, when there existed close genetic relatedness between the training and validation populations, higher prediction accuracies were observed compared to scenarios when they were genetically distant. These findings provide valuable resistance candidate genes and germplasm resources and are crucial for the application of genomic selection for breeding durable BPH-resistant rice varieties.

P458 Diagnostic accuracy of patency capsule and cross-sectional imaging for capsule endoscopy retention: A systematic review and meta-analysis

Abstract Background Although small bowel capsule endoscopy (SBCE) has been widely used to evaluate small bowel lesions including Crohn’s disease, there is a risk of capsule retention that can cause obstructive symptoms potentially necessitating surgical treatment. We conducted a systematic review and meta-analysis to evaluate the accuracy of patency capsule (PC) and cross-sectional imaging (CSI) for predicting capsule retention. Methods MEDLINE/PubMed, EMBASE, Web of science, and the Cochrane library databases were searched for articles that invested the diagnostic accuracy of PC or CSI for predicting capsule retention up to August 15, 2023. Pooled sensitivity and specificity were calculated using a bivariate random-effects model. Considering that SBCE was generally not performed when capsule retention was suspected on PC, the false-negative rates of PC and CSI were separately analyzed and compared using a single-proportion meta-analysis based on random-effects modeling. Quality assessment of the selected studies was performed using the revised Quality Assessment of Diagnostic Accuracy Studies-2 tool. Results A total of 22 peer-reviewed articles, including 17 articles for PC (covering 2,234 patients) and 7 articles for CSI (covering 302 patients) were included in this systematic review. The pooled sensitivity and specificity for predicting capsule retention with CSI were 64% [95% confidence interval (CI), 31%–87%] and 85% [95% CI, 62%–95%], respectively. The pooled sensitivity and specificity for predicting capsule retention with PC were 75% [95% CI, 43%–92%] and 94% [95% CI, 90%–96%], respectively (Figure 1, Table 1). Although there was no statistically significant difference in prediction accuracy between PC and CSI on the bivariate random-effects model (P=0.14), the pooled false-negative rate was significantly lower in the PC compared to CSI (2.5% [95% CI, 1.4%–4.7%] vs. 11.0% [95% CI, 5.2%–21.8%]; P=0.003) (Table 1). Conclusion Cross-sectional imaging showed good specificity, but showed moderate sensitivity in predicting capsule retention. In contrast, patency capsule showed good sensitivity and excellent specificity, with a significantly lower false-negative rate compared to cross-sectional imaging. Therefore, in patients with clinically suspicious small bowel stenosis, the patency capsule is a more reliable modality for predicting capsule retention.

PSMA PET/CT for Response Assessment and Overall Survival Prediction in Patients with Metastatic Castration-Resistant Prostate Cancer Treated with Androgen Receptor Pathway Inhibitors.

We aimed to evaluate the role of prostate-specific membrane antigen (PSMA) PET/CT for response assessment and outcome prediction in patients with metastatic castration-resistant prostate cancer (mCRPC) treated with androgen receptor pathway inhibitors (ARPIs), including abiraterone acetate or enzalutamide. Methods: We retrospectively analyzed 30 ARPI-treated mCRPC patients who underwent 68Ga-PSMA-11 PET/CT within 8 wk before (baseline) and 12 ± 4 wk after treatment initiation. Total PSMA tumor volume was calculated using the fixed threshold method (SUV ≥ 3). Patients were categorized as PSMA responders (PSMA-Rs) or PSMA nonresponders (PSMA-NRs) on the basis of both European Association of Urology/European Association of Nuclear Medicine (EAU/EANM) criteria and Response Evaluation Criteria in PSMA PET/CT (RECIP) 1.0. PSMA-R included patients with a complete response, a partial response, or stable disease, and PSMA-NR included those with progressive disease. On the basis of prostate-specific antigen (PSA), patients were classified as biochemical responders if PSA decreased by at least 50% and as nonresponders if it did not. The Φ-coefficient was used to evaluate the correlation of PSMA- and PSA-based responses. Survival analysis was performed using the Cox regression hazard model and the Kaplan-Meier method. Predictive accuracy was tested for both response criteria. Results: On the basis of PSMA PET/CT, 13 (43%) patients were PSMA-NR according to the EAU/EANM criteria and 11 (37%) patients were PSMA-NR according to RECIP 1.0. Significant correlations were observed between PSMA- and PSA-based responses for both criteria (Φ = 0.79 and 0.66, respectively). After a median follow-up of 25 mo (interquartile range, 21-43 mo), the median overall survival was significantly longer for PSMA-R than PSMA-NR (54 vs. 22 mo) for both the EAU/EANM criteria and RECIP 1.0, with hazard ratios of 6.9 (95% CI, 1.9-26; P = 0.004) and 5.6 (95% CI, 1.69-18.26, P = 0.005), respectively. No significant difference in predictive accuracy was found between the 2 criteria (C-index, 0.79 vs. 0.76, respectively, P = 0.54). Flare phenomena at the second PSMA PET study were not observed in our cohort. Conclusion: Our results demonstrate that PSMA PET/CT is a valuable imaging biomarker for response assessment and overall survival prediction when performed at 3 mo after ARPI treatment initiation in mCRPC patients. Both proposed PSMA response criteria (EAU/EANM and RECIP 1.0) seem to perform equally well. No PSMA flare was observed. Prospective validation of these findings is strongly needed.

Machine learning for effectively avoiding overfitting is a crucial strategy for the genetic prediction of polygenic psychiatric phenotypes

The accuracy of previous genetic studies in predicting polygenic psychiatric phenotypes has been limited mainly due to the limited power in distinguishing truly susceptible variants from null variants and the resulting overfitting. A novel prediction algorithm, Smooth-Threshold Multivariate Genetic Prediction (STMGP), was applied to improve the genome-based prediction of psychiatric phenotypes by decreasing overfitting through selecting variants and building a penalized regression model. Prediction models were trained using a cohort of 3685 subjects in Miyagi prefecture and validated with an independently recruited cohort of 3048 subjects in Iwate prefecture in Japan. Genotyping was performed using HumanOmniExpressExome BeadChip Arrays. We used the target phenotype of depressive symptoms and simulated phenotypes with varying complexity and various effect-size distributions of risk alleles. The prediction accuracy and the degree of overfitting of STMGP were compared with those of state-of-the-art models (polygenic risk scores, genomic best linear-unbiased prediction, summary-data-based best linear-unbiased prediction, BayesR, and ridge regression). In the prediction of depressive symptoms, compared with the other models, STMGP showed the highest prediction accuracy with the lowest degree of overfitting, although there was no significant difference in prediction accuracy. Simulation studies suggested that STMGP has a better prediction accuracy for moderately polygenic phenotypes. Our investigations suggest the potential usefulness of STMGP for predicting polygenic psychiatric conditions while avoiding overfitting.

Combined cycle gas turbine power output prediction and data mining with optimized data matching algorithm

Electrical power output (PE) for a combined cycle gas turbine (CCGT) consisting of 9568 data records collected over a 6-year period is evaluated by the transparent open box (TOB) machine-learning method to provide accurate PE predictions and insight to prediction errors. The PE predictions derived by applying the TOB optimized data matching technique are more accurate than published predictions for the dataset from fifteen correlation-based, machine-learning algorithms. TOB achieves this high-accuracy using a tuning subset of < 150 (~ 1.5%) data records. Its accuracy is confirmed by testing the optimized solutions against all dataset records in 15 runs spread across five shuffled datasets. The dataset has a few extreme outliers associated with its four independent variables and these negatively impact the prediction accuracy of machine-learning methods. Through its transparency and forensic-like auditability of its calculations for individual data records, the TOB algorithm is able to mine the dataset to provide useful insight to the interactions of the outliers with other data records. This enables a filtered dataset (9533 records), excluding 35 carefully selected data records, to be customized to deliver much improved prediction accuracy (RMSE = 2.89 MW). Mining the dataset also reveals significant differences in prediction accuracy achieved for different sectors of the PE distribution. This insight identifies that prediction accuracy could be further improved by dividing the dataset into separately optimized subsets, three along its main PE trend plus a fourth, small subset consisting of the outliers. The TOB algorithm demonstrates its value as a machine-learning tool capable of generating accurate predictions and easily auditable data mining. It is well suited for CCGT efficiency and performance optimization.

Accuracy of biometric formulae for intraocular lens power calculation in a teaching hospital.

To evaluate the accuracy of three commonly used biometric formulae across different axial lengths (ALs) at one United States Veterans Affairs teaching hospital. A retrospective chart review was conducted from November 2013 to May 2018. One eye of each patient who underwent cataract surgery with a monofocal intraocular lens (IOL) was included. The range of postoperative follow-up period was from 3wk to 4mo. The Holladay 2, Barrett Universal II, and Hill-Radial Basis Function (Hill-RBF) formulae were used to predict the postoperative refraction for all cataract surgeries. For each formula, we calculated the prediction errors [including mean absolute prediction error (MAE)] and the percentage of eyes within ±0.25 diopter (D) and ±0.5 D of predicted refraction. We performed subgroup analyses for short (AL<22.0 mm), medium (AL 22.0-25.0 mm), and long eyes (AL>25.0 mm). A total of 1131 patients were screened, and 909 met the inclusion criteria. Resident ophthalmologists were the primary surgeons in 710 (78.1%) cases. We found no statistically significant difference in predictive accuracy among the three formulae over the entire AL range or in the short, medium, and long eye subgroups. Across the entire AL range, the Hill-RBF formula resulted in the lowest MAE (0.384 D) and the highest percentage of eyes with postoperative refraction within ±0.25 D (42.7%) and ±0.5 D (75.5%) of predicted. All three formulae had the highest MAEs (>0.5 D) and lowest percentage within ±0.5 D of predicted refraction (<55%) in short eyes. In cataract surgery patients at our teaching hospital, three commonly used biometric formulae demonstrate similar refractive accuracy across all ALs. Short eyes pose the greatest challenge to predicting postoperative refractive error.

Further Assessment of the Relay Hepatocyte Assay for Determination of Intrinsic Clearance of Slowly Metabolised Compounds Using Radioactivity Monitoring and LC-MS Methods.

Low-clearance drugs are widely used by industry mostly because of their often longer half-life, allowing for lower or less frequent dosing. Nevertheless, prediction of human clearance for these molecules from in vitro models presents a great challenge for pharmaceutical scientists. The objective of this study was to further characterise the predictive accuracy of the relay hepatocyte assay using 14C and 3H labelled proprietary compounds with a low extraction ratio and the known clearance mechanism in rats. Highly permeable compounds cleared by metabolism as well as rate limitation by transport were included in this study. Blood clearance was determined from concentration-time profiles following intravenous dosing to rats. In vitro clearance was determined from the single concentration parent depletion-time profiles throughout the incubation period of up to 20h (five relays) using radioactivity monitoring in tandem with mass spectrometry. A new approach was proposed to correct concentrations for loss and dilution during the relay steps. Clearance was predicted with a standard well-stirred model for the liver and predicted values were then compared with observed data to evaluate method accuracy. The results showed that intrinsic clearance values predicted using the relay hepatocyte assay from either radioactivity or mass spectrometry concentration data were comparable. A significant difference in prediction accuracy between the permeable compounds cleared by hepatic metabolism (about 2-fold) and the compound that was the hepatic uptake substrate (5- to 6-fold of actual) was demonstrated. The relay method is effective in predicting in vivo clearance for the compounds that are cleared via hepatic metabolism but tends to be notably underpredictive for drugs that rely on uptake transport. Consistent with the overall trend toward underprediction of hepatic clearance from the in vitro models prevalently used in the pharmaceutical industry, all values predicted from the hepatocyte relay method were lower than observed.

Prediction of students’ awareness level towards ICT and mobile technology in Indian and Hungarian University for the real-time: preliminary results

An experimental study was conducted to predict the student's awareness of Information and Communication Technology (ICT) and Mobile Technology (MT) in Indian and Hungarian university's students. A primary dataset was gathered from two popular universities located in India and Hungary in the academic year 2017–2018. This paper focuses on the prediction of two major parameters from dataset such as usability and educational benefits using four machine learning classifiers multilayer perceptron (ANN), Support vector machine (SVM), K-nearest neighbor (KNN) and Discriminant (DISC). The multi-classification problem was solved with test, train and validated datasets using machine learning classifiers. One hand, feature aggregation with the train-test-validation technique improved the ANN's prediction accuracy of educational benefits for both countries. Another hand, ANN's accuracy decreases significantly in the prediction of usability. Further, SVM and ANN outperformed the KNN and the DISC in the prediction of awareness level towards ICT and MT in India and Hungary. Also, this paper reveals that the future awareness level for the educational benefits will be Very High or Moderate in both countries. Also, the awareness level is predicted as High and Moderate for usability parameter in both countries. Further, ANN and SVM accuracy and prediction time is compared with T-test at 0.05 significance level which distinguished CPU training time is taken by ANN and SVM using K-fold and Hold out method. Also, K-fold enhanced the significant prediction accuracy of SVM and ANN. the authors also used a STAC web platform to compare the accuracy datasets using T-test and ANOVA test at 0.05 significant level and we found ANN and SVM classifier has no significant difference in prediction accuracy in each dataset. Also, the authors recommend presented predictive models to be deployed as a real-time module of the institute's website for the real-time prediction of ICT & MT awareness level.

Predicting soft tissue changes after orthognathic surgery: The sparse partial least squares method.

To develop a prediction algorithm for soft tissue changes after orthognathic surgery that would result in accurate predictions (1) regardless of types or complexity of operations and (2) with a minimum number of input variables. The subjects consisted of 318 patients who had undergone the surgical correction of Class II or Class III malocclusions. Two multivariate methods-the partial least squares (PLS) and the sparse partial least squares (SPLS) methods-were used to construct prediction equations. While the PLS prediction model included 232 input variables, the SPLS method included a reduced number of variables generated by a handicapping algorithm via the sparsity control. The accuracy between the PLS and SPLS models was compared. There were no significant differences in prediction accuracy depending on surgical movements, the sex of the subjects, or additional surgeries. The predictive performance with a reduced set of 34 input variables chosen using the SPLS method was statistically indistinguishable from the full set of variables with the original PLS prediction model. The prediction method proposed in the present study was accurate for a wide range of orthognathic surgeries. A reduced set of input variables could be selected through the SPLS method while simultaneously maintaining a prediction level that was as accurate as that of the original PLS prediction model.

Predicting Learning Outcomes with MOOC Clickstreams

Massive Open Online Courses (MOOCs) have gradually become a dominant trend in education. Since 2014, the Ministry of Education in Taiwan has been promoting MOOC programs, with successful results. The ability of students to work at their own pace, however, is associated with low MOOC completion rates and has recently become a focus. The development of a mechanism to effectively improve course completion rates continues to be of great interest to both teachers and researchers. This study established a series of learning behaviors using the video clickstream records of students, through a MOOC platform, to identify seven types of cognitive participation models of learners. We subsequently built practical machine learning models by using K-nearest neighbor (KNN), support vector machines (SVM), and artificial neural network (ANN) algorithms to predict students’ learning outcomes via their learning behaviors. The ANN machine learning method had the highest prediction accuracy. Based on the prediction results, we saw a correlation between video viewing behavior and learning outcomes. This could allow teachers to help students needing extra support successfully pass the course. To further improve our method, we classified the course videos based on their content. There were three video categories: theoretical, experimental, and analytic. Different prediction models were built for each of these three video types and their combinations. We performed the accuracy verification; our experimental results showed that we could use only theoretical and experimental video data, instead of all three types of data, to generate prediction models without significant differences in prediction accuracy. In addition to data reduction in model generation, this could help teachers evaluate the effectiveness of course videos.

Accuracy of Gene Expression Prediction From Genotype Data With PrediXcan Varies Across and Within Continental Populations.

Using genetic data to predict gene expression has garnered significant attention in recent years. PrediXcan has become one of the most widely used gene-based methods for testing associations between predicted gene expression values and a phenotype, which has facilitated novel insights into the relationship between complex traits and the component of gene expression that can be attributed to genetic variation. The gene expression prediction models for PrediXcan were developed using supervised machine learning methods and training data from the Depression Genes and Networks (DGN) study and the Genotype-Tissue Expression (GTEx) project, where the majority of subjects are of European descent. Many genetic studies, however, include samples from multi-ethnic populations, and in this paper we evaluate the accuracy of PrediXcan for predicting gene expression in diverse populations. Using transcriptomic data from the GEUVADIS (Genetic European Variation in Disease) RNA sequencing project and whole genome sequencing data from the 1000 Genomes project, we evaluate and compare the predictive performance of PrediXcan in an African population (Yoruban) and four European ancestry populations for thousands of genes. We evaluate a range of models from the PrediXcan weight databases and use Pearson's correlation coefficient to assess gene expression prediction accuracy with PrediXcan. From our evaluation, we find that the predictive performance of PrediXcan varies substantially among populations from different continents (F-test p-value < 2.2 × 10−16), where prediction accuracy is lower in the Yoruban population from West Africa compared to the European-ancestry populations. Moreover, not only do we find differences in predictive performance between populations from different continents, we also find highly significant differences in prediction accuracy among the four European ancestry populations considered (F-test p-value < 2.2 × 10−16). Finally, while there is variability in prediction accuracy across different PrediXcan weight databases, we also find consistency in the qualitative performance of PrediXcan for the five populations considered, with the African ancestry population having the lowest accuracy across databases.

Wednesday, September 26, 2018 10:35 AM – 12:00 PM Cervical Spine Trauma: 22. A simplified clinical prediction rule for prognosticating independent walking after spinal cord injury: a prospective study from a Canadian multicenter spinal cord injury registry

BACKGROUND CONTEXT Models for predicting recovery in patients affected with traumatic spinal cord injury (tSCI) have been developed in recent years to optimize patient-centered care. Several databases and analytical models have been validated in their ability to predict tSCI recovery, yet recent findings have queried the accuracy of these models in patients whose prognoses are least predictable, ie, AIS B and C patients. PURPOSE To examine the predictive accuracy of van Middendorp's five-variable and Hicks’ three-variable LR models in the subset of AIS B and C patients. Secondly, to assess the predictive accuracy of a novel, seven-variable logistic regression model in this cohort. Thirdly, to investigate the utility of advanced computing models based on artificial intelligence in the context of AIS B and C prognostication. STUDY DESIGN/SETTING Prospective cohort study using the Rick Hansen Spinal Cord Injury Registry, a pan-Canadian prospective observational registry. PATIENT SAMPLE Patients who sustained tSCI between 2004 and 2016. OUTCOME MEASURES Neurological severity (AIS) at time of admission and level of injury was assessed within 15 days of injury according to the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI). Independent walking ability was assessed by the FIM at ≥12 months from time of injury, by which time neurologic and functional outcomes are known to plateau (16-20). METHODS Three logistic regression models and two artificial intelligence models were evaluated for their ability to predict walking ability one-year postinjury. Comparison of prognostic performance between models was calculated by area under the receiver operating characteristic curve (AUC). RESULTS A total of 675 tSCI patients within the RHSCIR were identified for our study. From 160 AIS B and C patients, our modified LR model demonstrated an AUC of 0.854 (95% CI 0.808–0.900). For the same cohort, van Middendorp et al.’s LR model generated an AUC of 0.833 (95% CI 0.771–0.895), while the artificial intelligence model presenting the highest degree of prognostic ability (neural networking) generated an AUC of 0.872. There were no significant differences in predictive accuracy between models (p>.05). The AUC for 515 AIS A and D patients utilizing van Middendorp et al.’s LR model was 0.954. CONCLUSIONS Application of our novel, seven-variable LR model, two previously tested LR models, and two artificial intelligence models demonstrated limited predictive accuracy for AIS B and C patients. These findings suggest that former models achieved strong prognostic accuracy through a high proportion of AIS A and D patients in their analysis cohort; furthermore, present models cannot effectively predict ambulatory outcomes in AIS B and C patients, regardless of methodology. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

Pathological staging criteria for carcinoma in the remnant stomach: an exploratory study

To explore the prognostic value of the tumor-ratio-metastasis (TRM) staging system for carcinoma in the remnant stomach(CRS). Clinicopathological data of 91 CRS patients who underwent surgery at Peking University People's Hospital between March 1992 and December 2017 were retrospectively analyzed. According to the ratio of metastatic lymph node to dissected lymph node, the R staging was obtained, and the pN staging was replaced by the R staging to create the TRM staging. To compare the predictive accuracy of TRM and tumor-node-metastasis (TNM, UICC version 7), the R staging and pN staging were included in the prognostic factor analysis model, and the survival curve, c-index, and 95% confidence interval (CI) of the TRM staging and TNM staging system were compared. A higher c-index value means higher prediction accuracy. Of 91 CRS patients, 77 were male and 14 were female with the mean onset age of (65.2±10.4) years. The mean interval from the first operation to CRS onset was 156(6-600) months. The primary diseases of 49(53.8%) cases were benign and of 42(46.2%) cases were malignant. The median number of retrieved lymph node (RLN) was 8 (0-38), and 64 patients (70.3%) had an RLN ≤15. Lymph node metastasis occurred in 50 patients (54.9%). pN staging result was as follows: 41 cases in N0 stage, 14 in N1 stage, 19 in N2 stage, and 17 in N3 stage. R staging result was as follows: 41 cases in R0 stage, 4 in R1 stage, 19 in R2 stage, and 27 in R3 stage. TNM staging result was as follows: 13 cases in stage I(, 25 in stage II(, 10 in stage III(a, 23 in stage III(b, and 6 in stage III(c. TRM staging result was as follows: 13 cases in stage I(, 24 in stage II(, and 4 in stage III(a, 18 in stage III(b, and 18 in stage III(c. Univariate analysis showed that tumor diameter ≥7 cm (HR=2.696, 95%CI: 1.307-5.563, P=0.007), T3-4 stage (HR=4.350, 95%CI: 1.949-9.707, P=0.000), N2-3 stage (HR=1.883, 95%CI: 1.167-3.038, P=0.009), R2-3 stage (HR=1.642, 95%CI: 1.026-2.628, P=0.039), TNM III(-IIII( stage (HR=2.448, 95%CI:1.490-4.021, P=0.000), and TRM III(-IIII( stage (HR=2.504, 95%CI:1.515-4.137, P=0.000) were related to prognosis. Tumor diameter, pT staging, and pN staging were included in the Cox multivariate analysis, and the result showed that pT staging (HR=5.507, 95%CI:2.254-13.454, P=0.000) and pN staging (HR=1.698, 95%CI: 1.022-2.789, P=0.041) were independent risk factors for overall survival of CRS in this group. While R staging replaced pN staging and was included in the Cox multivariate analysis together with tumor diameter and pT staging, the result showed that R staging was not an independent risk factor for CRS in this group (HR=1.622, 95%CI: 0.866-2.329, P=0.164). Survival curve revealed pN and TNM staging systems provided better stratified curves according to each staging than R and TRM staging systems. The overall survival c-index of TNM and TRM staging systems was 0.813(95%CI: 0.732-0.826) and 0.809(95%CI: 0.741-0.847) respectively, and no significant difference in predictive accuracy was found (P=0.693). In 42 patients with primary malignance, the overall survival c-index of TNM and TRM staging systems was 0.774(95%CI: 0.589-0.901) and 0.761(95%CI: 0.596-0.912) respectively, and there was no significant difference in predictive accuracy as well (P=0.881). TRM staging is not superior to TNM staging (7th UICC) in evaluating the resected samples of CRS.

A frequency-domain approach to improve ANNs generalization quality via proper initialization

The ability to train a network without memorizing the input/output data, thereby allowing a good predictive performance when applied to unseen data, is paramount in ANN applications. In this paper, we propose a frequency-domain approach to evaluate the network initialization in terms of quality of training, i.e., generalization capabilities. As an alternative to the conventional time-domain methods, the proposed approach eliminates the approximate nature of network validation using an excess of unseen data. The benefits of the proposed approach are demonstrated using two numerical examples, where two trained networks performed similarly on the training and the validation data sets, yet they revealed a significant difference in prediction accuracy when tested using a different data set. This observation is of utmost importance in modeling applications requiring a high degree of accuracy. The efficiency of the proposed approach is further demonstrated on a real-world problem, where unlike other initialization methods, a more conclusive assessment of generalization is achieved. On the practical front, subtle methodological and implementational facets are addressed to ensure reproducibility and pinpoint the limitations of the proposed approach.

Comparison of CRUSADE and ACUITY-HORIZONS Bleeding Risk Scores in Patients With Acute Coronary Syndromes

Compare the discriminative performance of two validated bleeding risk models for in-hospital bleeding events in a non-selected cohort of acute coronary syndrome (ACS) patients. CRUSADE (Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the ACC/AHA Guidelines) and ACUITY-HORIZONS (Acute Catheterization and Urgent Intervention Triage strategY-Harmonizing Outcomes with Revascularization and Stents in Acute Myocardial Infarction) scores were calculated in 501 consecutive patients (median age 68 years (IQR 57-77), 31% female) admitted for ACS to the coronary care unit (CCU) of San Paolo Hospital in Milan (Italy). In-hospital haemorrhagic events and mortality were recorded and calibration and discrimination of the two risk models were evaluated using the Hosmer-Lemeshow test and the C-statistic, respectively. Overall bleeding events were observed in 32 patients and major bleedings in 11 (with an incidence of 6.4% and 2.2%, respectively). In-hospital mortality was 2.6%. Regarding major bleedings both risk scores demonstrated an adequate calibration (H-L test p>0.20) and a moderate discrimination with no significant difference in predictive accuracy between the two models (C-statistic 0.69 for CRUSADE and 0.73 for ACUITY-HORIZONS). We also tested the performance of the two risk models in predicting in-hospital mortality, showing an adequate calibration and a very good discrimination (C-statistic 0.88 and 0.89 for the CRUSADE and ACUITY-HORIZONS scores, respectively), with no significant difference in predictive accuracy. In our ACS population the CRUSADE and the ACUITY-HORIZONS risk scores showed a fairly good and comparable predictive accuracy regarding in-hospital bleeding events and they appeared to be very good predictors of in-hospital mortality.

Parameter Estimation for Predictive Simulation of Oscillatory Systems with Model Discrepancy

Abstract: A Bayesian framework provides a methodology in which inferences from measurement data can be used to bound the uncertainties in the predictive simulation of a physical system. The accuracy of these bounds relies on the satisfaction of statistical assumptions on the measurement error. Discrepancies between the model and the true physics can invalidate these assumptions. We examine the effect of such model discrepancies in the context of an oscillating cantilever beam. First we illustrate the influence of discrepancies in a simplified model of purely periodic signals and then we observe how discrepancies affect the accuracy of prediction uncertainty bounds using Bayesian parameter inference on a Euler-Bernoulli beam model. Our study shows small changes in the inference setup can result in significant differences in prediction accuracy and calls attention to important considerations for the practical application of Bayesian parameter estimation.

Prognostic value of computed tomography based SYNTAX score in coronary artery disease

I have read the article with great interest by Suh et al. [ [1] Young Joo Suh Yoo Jin Hong Hye-Jeong Lee Jin Hur Young Jin Kim Hye Sun Lee Sae Rom Hong Dong Jin Im Yun Jung Kim Chul Hwan Park Tae Hoon Kim Kyung-Jong Yoo ByoungWook Choi Prognostic value of SYNTAX score based on coronary computed tomography angiography. Int. J. Cardiol. 2015; 199: 460-466 Abstract Full Text Full Text PDF PubMed Scopus (15) Google Scholar ] recently published online in your journal. In their study, the authors reported that computed tomography (CT)-based SYNTAX score has prognostic value for predicting major adverse cardiac events (MACEs) in patients who underwent both coronary CT angiography and invasive coronary angiography for suspected coronary artery disease (CAD), and CT-based SYNTAX score showed no significant difference in predictive accuracy compared with invasive coronary angiography-based SYNTAX score.

Predictive Validity of the J-SOAP-II: Does Accuracy Differ Across Settings?

Court and mental health workers are frequently asked to determine which juvenile sex offenders (JSOs) are most likely to reoffend. One instrument commonly used to guide decision making with JSOs is the Juvenile Sex Offender Assessment Protocol-II (J-SOAP-II). However, research utilizing this instrument has often generated contradictory results, perhaps related to the types of samples studied. The current study sought to compare the predictive accuracy of the J-SOAP-II across two samples of JSOs (a medium-security correctional setting versus an unlocked residential sex offender treatment program). Although the overall predictive accuracy for identifying post-release arrests for sexual offenses (i.e., sexual recidivism) was modest (AUC = .64) and not statistically significant, differences emerged with regard to the accuracy of some individual scales and subscales. Similarly, while no significant differences in predictive accuracy were observed between the two study sites, a number of interesting findings were observed. These findings highlight the need to consider risk assessment measures in light of the setting in which they are used in order to maximize predictive accuracy and optimize treatment and dispositional decision making.

Finite element analysis and CT-based structural rigidity analysis to assess failure load in bones with simulated lytic defects

There is an urgent need to improve the prediction of fracture risk for cancer patients with bone metastases. Pathological fractures that result from these tumors frequently occur in the femur. It is extremely difficult to determine the fracture risk even for experienced physicians. Although evolving, fracture risk assessment is still based on inaccurate predictors estimated from previous retrospective studies. As a result, many patients are surgically over-treated, whereas other patients may fracture their bones against expectations.We mechanically tested ten pairs of human cadaveric femurs to failure, where one of each pair had an artificial defect simulating typical metastatic lesions. Prior to testing, finite element (FE) models were generated and computed tomography rigidity analysis (CTRA) was performed to obtain axial and bending rigidity measurements. We compared the two techniques on their capacity to assess femoral failure load by using linear regression techniques, Student's t-tests, the Bland–Altman methodology and Kendall rank correlation coefficients.The simulated FE failure loads and CTRA predictions showed good correlation with values obtained from the experimental mechanical testing. Kendall rank correlation coefficients between the FE rankings and the CTRA rankings showed moderate to good correlations. No significant differences in prediction accuracy were found between the two methods.Non-invasive fracture risk assessment techniques currently developed both correlated well with actual failure loads in mechanical testing suggesting that both methods could be further developed into a tool that can be used in clinical practice. The results in this study showed slight differences between the methods, yet validation in prospective patient studies should confirm these preliminary findings.