Best Set Of Predictors Research Articles

Fully General Chao and Zelterman Estimators with Application to a Whale Shark Population

Summary We introduce generalized Chao and generalized Zelterman estimators which include individual, time varying and behavioural effects. Under mild assumptions in the presence of unobserved heterogeneity, the generalized Chao estimator asymptotically provides a lower bound for the population size and is unbiased otherwise. Corrected versions guarantee bounded estimates. To include the best set of predictors we propose the biased empirical focused information criterion bFIC. Simulations indicate that bFIC might give considerable improvements over other selection criteria in our context. We illustrate with an original application to size estimation of a whale shark (Rhincodon typus) population in South Ari Atoll, in the Maldives.

Selecting predictors to maximize the transferability of species distribution models: lessons from cross‐continental plant invasions

AbstractAimNiche‐based species distribution models (SDMs) are commonly used to predict impacts of global change on biodiversity, but the reliability of these predictions in space and time depends on their transferability. We tested how the strategy used to choose predictors impacts the transferability of SDMs at a cross‐continental scale.LocationNorth America, Eurasia and Australia.MethodWe used a systematic approach including 50 Holarctic plant invaders and 27 initial predictor variables, considering 10 different strategies for variable selection, accounting for the proximality, multicollinearity and climate analogy of predictors. We compared the average performance of each strategy, some of which used a large number of predictor combinations. Next, we looked for the single best model for each species across all the predictor combinations retained in the analysis. Transferability was considered as the predictive success of SDMs calibrated in the native range and projected onto the invaded range.ResultsTwo strategies showed better SDM transferability on average: a set of predictors known for their ecologically meaningful effects on plant distribution, and the two first axes of a principal component analysis calibrated on all predictor variables (Spc2). From the more than 2000 combinations of predictors per species across strategies, the best set of predictors yielded SDMs with good transferability for 45 species (90%). These best combinations consisted of eight randomly assembled (39 species) or uncorrelated predictors (6 species) and Spc2 (5 species). We also found that internal cross‐validation was not sufficient to give full information about the transferability of a SDM to a distinct range.Main conclusionTransferring SDMs at the macroclimatic scale, and thus anticipating invasions, is possible for the large majority of invasive plants considered in this study, but the accuracy of the predictions relies strongly on the choice of predictors. From our results, we recommend including either proximal and state‐of‐the‐art variables or a reduced and orthogonalized set to obtain robust SDM projections.

Predicting Breast Cancer Recurrence Using Machine Learning Techniques

Background: Recurrence is an important cornerstone in breast cancer behavior, intrinsically related to mortality. In spite of its relevance, it is rarely recorded in the majority of breast cancer datasets, which makes research in its prediction more difficult. Objectives: To evaluate the performance of machine learning techniques applied to the prediction of breast cancer recurrence. Material and Methods: Revision of published works that used machine learning techniques in local and open source databases between 1997 and 2014. Results: The revision showed that it is difficult to obtain a representative dataset for breast cancer recurrence and there is no consensus on the best set of predictors for this disease. High accuracy results are often achieved, yet compromising sensitivity. The missing data and class imbalance problems are rarely addressed and most often the chosen performance metrics are inappropriate for the context. Discussion and Conclusions: Although different techniques have been used, prediction of breast cancer recurrence is still an open problem. The combination of different machine learning techniques, along with the definition of standard predictors for breast cancer recurrence seem to be the main future directions to obtain better results.

An efficient multi-predictor reversible data hiding algorithm based on performance evaluation of different prediction schemes

With the broad development and evolution of digital data exchange, security has become an important issue in data storage and transmission since digital data can be easily manipulated and modified. Reversible data hiding algorithms are special class of steganography that are capable of recovering the original cover image upon the extraction of the secret data. This issue is of interest in medical and military imaging applications. Many algorithms in this class exploit the idea of prediction in order to increase the embedding capacity as well as the quality of the stego image. However, the performance of these algorithms depends on the type of predictor that is being used. The main goal in this paper is to survey different predictors and evaluate their performance when employed in two classical reversible data hiding algorithms. The evaluation considered plugging 22 predictors in the two algorithms to process 1438 test images. Experimental results validated the varying capabilities of different predictors and showed that the non-causal median predictor had the best performance in the two algorithms. Further more, the paper proposes a new multi-predictor reversible data hiding algorithm. Basically, the algorithm employs multiple predictors in an extended version of the modification of prediction errors (MPE) algorithm. The algorithm takes advantage of the results obtained from the performance evaluation of different predictors to select the best set of predictors. Performance evaluation proved the ability of the proposed algorithm in increasing the embedding capacity while maintaining high stego image quality.

Association of Trabecular Bone Score with Inflammation and Adiposity in Patients with Psoriasis: Effect of Adalimumab Therapy.

Studies on trabecular bone score (TBS) in psoriasis are lacking. We aim to assess the association between TBS and inflammation, metabolic syndrome features, and serum adipokines in 29 nondiabetic patients with psoriasis without arthritis, before and after 6-month adalimumab therapy. For that purpose, adjusted partial correlations and stepwise multivariable linear regression analysis were performed. No correlation was found between TBS and disease severity. TBS was negatively associated with weight, BMI, waist perimeter, fat percentage, and systolic and diastolic blood pressure before and after adalimumab. After 6 months of therapy, a negative correlation between TBS and insulin resistance (p = 0.02) and leptin (p = 0.01) and a positive correlation with adiponectin were found (p = 0.01). The best set of predictors for TBS values at baseline were female sex (p = 0.015), age (p = 0.05), and BMI (p = 0.001). The best set of predictors for TBS following 6 months of biologic therapy were age (p = 0.001), BMI (p < 0.0001), and serum adiponectin levels (p = 0.027). In conclusion, in nondiabetic patients with moderate-to-severe psoriasis, TBS correlates with metabolic syndrome features and inflammation. This association is still present after 6 months of adalimumab therapy. Moreover, serum adiponectin levels seem to be an independent variable related to TBS values, after adalimumab therapy.

Evaluation of the platelet cross-matching in oncology patients

Context: Platelet transfusion is an essential part of managing cancer. Refractoriness to platelet transfusion poses great challenge in the treatment of thrombocytopenic patients. Aims: The prospective study was undertaken to correlate and evaluate the result of the platelet cross-matching with posttransfusion count increment and to ascertain the effectiveness of routinely performing platelet cross-matching in Indian perspective. Subjects and Methods: ABO compatible, leukoreduced, random-donor platelets were randomly transfused to 30 thrombocytopenic, oncology patients. Platelet cross-match and platelet antibody screen were performed using solid-phase red-cell adherence (SPRCA) techniques and corrected count increment (CCI) was ascertained. Statistical Analysis: Statistical Package for Social Sciences version 16, Chi-square test were used for statistical analysis. Multivariate analysis and step-wise regression procedure were used to choose the set of best predictors. Results: One hundred and twenty-two units (86%) were negative cross-matched while 20 units (14%) were positive platelet cross-matched. Mean CCI was 28,927 ± 23,007 which was more and statistically significant in females than in males. Four cases (13.33%) showed platelet refractoriness with CCI Conclusion: Platelet cross-match using SPRCA is an effective and rapid first-line approach for selecting compatible platelets as compared to human leucocyte antigen-matched platelets in the treatment of thrombocytopenic cancer patients. Platelet cross-match along with testing for anti-platelet antibodies should be an important component in the management of oncology patients which is less time-consuming and cost-effective than the molecular testing.

Risk Factors Associated With Grade 3 Pivot Shift After Acute Anterior Cruciate Ligament Injuries

Background: Recent literature has demonstrated that involvement in pivoting sports at the time of injury, increased posterior-inferior tibial slope (PITS), anterolateral capsular ligament (ALCL) disruptions, and combined lateral meniscal lesions all contribute to a higher grade of pivot shift after acute anterior cruciate ligament (ACL) injuries. Purpose: To identify risk factors associated with grade 3 pivot shift after acute ACL injuries. Study Design: Case-control study; Level of evidence, 3. Methods: A database of patients undergoing primary ACL reconstruction from 2009 to 2015 after acute ACL injuries was retrospectively reviewed. A total of 30 patients (30 knees) with grade 3 pivot shift were identified as the study group. Moreover, 30 patients (30 knees) with grade 1 and 30 patients (30 knees) with grade 2 pivot shift were randomly chosen as the 2 control groups. All pivot-shift tests were performed with the patients under anesthesia. Predictors of grade 3 pivot shift that were explored included patient age, sex, time from injury to surgery, pivoting sports involvement at the time of injury, lateral PITS, medial PITS, ALCL disruptions, preoperative KT-1000 arthrometer side-to-side difference, and combined lateral or medial meniscal lesions. Comparisons were performed between the study group and the 2 control groups. Univariable and multivariable logistic regressions were used to identify independent risk factors that were associated with grade 3 pivot shift. Results: Univariable and multivariable logistic regressions between the study group and the 2 control groups showed that pivoting sports involvement at the time of injury (odds ratio1 [OR1; grade 3 vs grade 1 pivot shift with grade 1 = reference], 11.88; 95% CI, 1.94-149.91; OR2 [grade 3 vs grade 2 pivot shift with grade 2 = reference], 3.41; 95% CI, 1.92-22.60), abnormal degree of lateral PITS (OR1, 14.41; 95% CI, 1.69-174.78; OR2, 6.41; 95% CI, 1.48-47.70), ALCL disruptions (OR1, 8.28; 95% CI, 1.71-117.14; OR2, 4.96; 95% CI, 1.07-28.75), and combined lateral meniscal lesions (OR1, 27.56; 95% CI, 5.48-240.52; OR2, 5.83; 95% CI, 1.21-38.56) were independent risk factors of grade 3 pivot shift after acute ACL injuries. Conclusion: For acute ACL injuries, the best set of predictors of grade 3 pivot shift were pivoting sports involvement at the time of injury, abnormal lateral PITS, ALCL disruptions, and combined lateral meniscal lesions. These results may provide additional information for counseling patients on residual laxity and risks for graft rerupture after ACL reconstruction.

Are Mortality and Acute Morbidity in Patients Presenting With Nonspecific Complaints Predictable Using Routine Variables?

Patients presenting to the emergency department (ED) with nonspecific complaints are difficult to accurately triage, risk stratify, and diagnose. This can delay appropriate treatment. The extent to which key medical outcomes are at all predictable in these patients, and which (if any) predictors are useful, has previously been unclear. To investigate these questions, we tested an array of statistical and machine learning models in a large group of patients and estimated the predictability of mortality (which occurred in 6.6% of our sample of patients), acute morbidity (58%), and presence of acute infectious disease (28.2%). To investigate whether the best available tools can predict the three key outcomes, we fed data from a sample of 1,278 ED patients with nonspecific complaints into 17 state-of-the-art statistical and machine learning models. The patient sample stems from a diagnostic multicenter study with prospective 30-day follow-up conducted in Switzerland. Predictability of the three key medical outcomes was quantified by computing the area under the receiver operating characteristic curve (AUC) for each model. The models performed at different levels but, on average, the predictability of the target outcomes ranged between 0.71 and 0.82. The better models clearly outperformed physicians' intuitive judgments of how ill patients looked (AUC = 0.67 for mortality, 0.65 for morbidity, and 0.60 for infectious disease). Modeling techniques can be used to derive formalized models that, on average, predict the outcomes of mortality, acute morbidity, and acute infectious disease in patients with nonspecific complaints with a level of accuracy far beyond chance. The models also predicted these outcomes more accurately than did physicians' intuitive judgments of how ill the patients look; however, the latter was among the small set of best predictors for mortality and acute morbidity. These results lay the groundwork for further refining triage and risk stratification tools for patients with nonspecific complaints. More research, informed by whether the goal of a model is high sensitivity or high specificity, is needed to develop readily applicable clinical decision support tools (e.g., decision trees) that could be supported by electronic health records.

Risk calculators predict failures of knee and hip arthroplasties: findings from a large health maintenance organization.

Considering the cost and risk associated with revision Total knee arthroplasty (TKAs) and Total hip arthroplasty (THAs), steps to prevent these operations will help patients and reduce healthcare costs. Revision risk calculators for patients may reduce revision surgery by supporting clinical decision-making at the point of care. We sought to develop a TKA and THA revision risk calculator using data from a large health-maintenance organization's arthroplasty registry and determine the best set of predictors for the revision risk calculator. Revision risk calculators for THAs and TKAs were developed using a patient cohort from a total joint replacement registry and data from a large US integrated healthcare system. The cohort included all patients who had primary procedures performed in our healthcare system between April 2001 and July 2008 and were followed until January 2014 (TKAs, n = 41,750; THAs, n = 22,721), During the study period, 9% of patients (TKA = 3066/34,686; THA=1898/20,285) were lost to followup and 7% died (TKA= 2350/41,750; THA=1419/20,285). The outcome of interest was revision surgery and was defined as replacement of any component for any reason within 5 years postoperatively. Candidate predictors for the revision risk calculator were limited to preoperative patient demographics, comorbidities, and procedure diagnoses. Logistic regression models were used to identify predictors and the Hosmer-Lemeshow goodness-of-fit test and c-statistic were used to choose final models for the revision risk calculator. The best predictors for the TKA revision risk calculator were age (odds ratio [OR], 0.96; 95% CI, 0.95-0.97; p < 0.001), sex (OR, 0.84; 95% CI, 0.75-0.95; p = 0.004), square-root BMI (OR, 1.05; 95% CI, 0.99-1.11; p = 0.140), diabetes (OR, 1.32; 95% CI, 1.17-1.48; p < 0.001), osteoarthritis (OR, 1.16; 95% CI, 0.84-1.62; p = 0.368), posttraumatic arthritis (OR, 1.66; 95% CI, 1.07-2.56; p = 0.022), and osteonecrosis (OR, 2.54; 95% CI, 1.31-4.92; p = 0.006). The best predictors for the THA revision risk calculator were sex (OR, 1.24; 95% CI, 1.05-1.46; p = 0.010), age (OR, 0.98; 95% CI, 0.98-0.99; p < 0.001), square-root BMI (OR, 1.07; 95% CI, 1.00-1.15; p = 0.066), and osteoarthritis (OR, 0.85; 95% CI, 0.66-1.09; p = 0.190). Study model parameters can be used to create web-based calculators. Surgeons can enter personalized patient data in the risk calculators for identification of risk of revision which can be used for clinical decision making at the point of care. Future prospective studies will be needed to validate these calculators and to refine them with time. Level III, prognostic study.

Prediction of Juvenile-Onset Myopia.

Myopia (nearsightedness) has its onset in childhood and affects about one-third of adults in the United States. Along with its high prevalence, myopia is expensive to correct and is associated with ocular diseases that include glaucoma and retinal detachment. To determine the best set of predictors for myopia onset in school-aged children. The Collaborative Longitudinal Evaluation of Ethnicity and Refractive Error (CLEERE) Study was an observational cohort study of ocular development and myopia onset conducted at 5 clinical sites from September 1, 1989, through May 22, 2010. Data were collected from 4512 ethnically diverse, nonmyopic school-aged children from grades 1 through 8 (baseline grades 1 through 6) (ages 6 through 13 years [baseline, 6 through 11 years]). We evaluated 13 candidate risk factors for their ability to predict the onset of myopia. Myopia onset was defined as -0.75 diopters or more of myopia in each principal meridian in the right eye as measured by cycloplegic autorefraction at any visit after baseline until grade 8 (age 13 years). We evaluated risk factors using odds ratios from discrete time survival analysis, the area under the curve, and cross validation. A total of 414 children became myopic from grades 2 through 8 (ages 7 through 13 years). Of the 13 factors evaluated, 10 were associated with the risk for myopia onset (P < .05). Of these 10 factors, 8 retained their association in multivariate models: spherical equivalent refractive error at baseline, parental myopia, axial length, corneal power, crystalline lens power, ratio of accommodative convergence to accommodation (AC/A ratio), horizontal/vertical astigmatism magnitude, and visual activity. A less hyperopic/more myopic baseline refractive error was consistently associated with risk of myopia onset in multivariate models (odds ratios from 0.02 to 0.13, P < .001), while near work, time outdoors, and having myopic parents were not. Spherical equivalent refractive error was the single best predictive factor that performed as well as all 8 factors together, with an area under the curve (C statistic) ranging from 0.87 to 0.93 (95% CI, 0.79-0.99). Future myopia can be predicted in a nonmyopic child using a simple, single measure of refractive error. Future trials for prevention of myopia should target the child with low hyperopia as the child at risk.

An Evaluation of Climate Change Impacts on Extreme Sea Level Variability: Coastal Area of New York City

Climate change has resulted in sea level rise and increasing frequency of extreme storm events around the world. This has intensified flood damage especially in coastal regions. In this study, a methodology is developed to analyze the impacts of climate change on sea level changes in the coastal regions utilizing an artificial neural network model. For simulation of annual extreme sea level, climate signals of Sea Surface Temperature, Sea Level Pressure and SLP gradient of the study region and some characteristic points are used as predictors. To select the best set of predictors as neural network model input, feature selection methods of MRMR (Minimum Redundancy Maximum Relevance) and MI (Mutual Information) are used. Future values of the selected predictors under greenhouse gas emission scenarios of B1, A1B and A2 are used in the developed neural network model to project water level for the next 100 years. Sea levels with different recurrence intervals are determined using frequency analysis of historical and projected water level as well, and the impact of climate change in extreme sea level is investigated. The developed methodology is applied to New York City to determine the coastal region vulnerability to water level changes. The results of this study show remarkable increase in sea level in the New York City, which is an indicative of coastal areas vulnerability and the need to take strategic actions in dealing with climate change.

The end‐of‐life experience: Modifiable predictors of caregivers' bereavement adjustment

The objective of the current study was to determine the best set of predictors of psychological disorders, regrets, health-related quality of life, and mental health function among bereaved caregivers of patients with cancer, thereby identifying promising targets for interventions to improve bereavement adjustment. Coping with Cancer is a longitudinal study of patients with advanced cancer and their informal caregivers who were enrolled from 2002 to 2008. The main outcome measure was bereavement adjustment of 245 caregivers (eg, depression, anxiety, and regrets) 6 months after the loss of the patient. The Structured Clinical Interview of the Diagnostic and Statistical Manual of Mental Disorders determined whether caregivers met the criteria for major depressive disorder or an anxiety disorder. Changes in health-related quality of life and mental health function from baseline to after the patient's death were assessed with the Medical Outcomes Study Short Form (SF-36). Greater than 50% of the caregivers reported regret about the cancer patient's end-of-life care; better patient quality of death (adjusted odds ratio, 0.77; 95% confidence interval, 0.67-0.88) reduced the risk of bereavement regret. The incidence of major depressive disorder or anxiety among the bereaved caregivers was 12.6% and was less likely for caregivers with better mental health before the loss of the patient (adjusted odds ratio, 0.03; 95% confidence interval, 0.004-0.25). Better patient quality of death also predicted improved caregiver health-related quality of life (adjusted standardized beta, .28; P < .001). The completion of a do-not-resuscitate order was found to be predictive of improved mental health from before the death of the patient to after the death (adjusted standardized beta, .29; P < .001). Reducing caregiver distress, encouraging advance care planning by patients, and improving patients' quality of death appear to be promising targets of interventions to improve caregiver bereavement adjustment.

Predicting the Distribution Pattern of Small Carnivores in Response to Environmental Factors in the Western Ghats

Due to their secretive habits, predicting the pattern of spatial distribution of small carnivores has been typically challenging, yet for conservation management it is essential to understand the association between this group of animals and environmental factors. We applied maximum entropy modeling (MaxEnt) to build distribution models and identify environmental predictors including bioclimatic variables, forest and land cover type, topography, vegetation index and anthropogenic variables for six small carnivore species in Mudumalai Tiger Reserve. Species occurrence records were collated from camera-traps and vehicle transects during the years 2010 and 2011. We used the average training gain from forty model runs for each species to select the best set of predictors. The area under the curve (AUC) of the receiver operating characteristic plot (ROC) ranged from 0.81 to 0.93 for the training data and 0.72 to 0.87 for the test data. In habitat models for F. chaus, P. hermaphroditus, and H. smithii “distance to village” and precipitation of the warmest quarter emerged as some of the most important variables. “Distance to village” and aspect were important for V. indica while “distance to village” and precipitation of the coldest quarter were significant for H. vitticollis. “Distance to village”, precipitation of the warmest quarter and land cover were influential variables in the distribution of H. edwardsii. The map of predicted probabilities of occurrence showed potentially suitable habitats accounting for 46 km2 of the reserve for F. chaus, 62 km2 for V. indica, 30 km2 for P. hermaphroditus, 63 km2 for H. vitticollis, 45 km2 for H. smithii and 28 km2 for H. edwardsii. Habitat heterogeneity driven by the east-west climatic gradient was correlated with the spatial distribution of small carnivores. This study exemplifies the usefulness of modeling small carnivore distribution to prioritize and direct conservation planning for habitat specialists in southern India.

Evaluation of isometric muscle strength and magnitude of hand dominance in right-handed school-age boys

The aim of this study was to examine bilateral isometric muscle strength (IMS) in right-handed boys. To determine the association between the magnitudes of right handedness (MRH) and (a) the interside difference indexes of IMS of certain muscle categories, (b) age, and (c) some anthropometric characteristics. This was a cross-sectional study. A convenience sample of 94 right-handed boys between 6 and 10 years of age was investigated. The Edinburgh Handedness Inventory was used to evaluate hand dominance and its magnitude. IMS of eight muscle groups was measured and the interside difference was determined. Five interside difference indexes were calculated for the upper limb, lower limb, hand grip, key pinch, and total side. All interside difference indexes of IMS were less than 5%, in favor of the dominant right side. The majority (86.1%) of our boys scored high to moderate MRH, whereas the minority (13.8%) scored low MRH. The hand grip interside difference index (β=0.40, P=0.000), the key pinch interside difference index (β=0.23, P=0.003), age (β=0.24, P=0.034), and height (β=0.33, P=0.039) are the best set of predictors of the dominance score and in turn the MRH. The results of this study, carried out on right-handed boys aged 6-10 years, indicated a standard difference in IMS between dominant and nondominant sides of all studied muscle strength categories. Furthermore, it was found that the hand grip interside difference index, the key pinch interside difference index, age, and height could influence the MRH.

Changes in cognitive function associated with syndrome changes on two five‐factor models of the Positive and Negative Syndrome Scale

This study aims to examine the association between neuropsychological function and symptom changes over time on two five-factor models, pentagonal (PM) and Van der Gaag (VDG), of the Positive and Negative Syndrome Scale (PANSS) and to determine the added value of these syndrome models for interpreting neuropsychological changes. Data were collected in a randomized controlled trial comparing second-generation and conventional antipsychotic medications for 108 adult patients diagnosed with schizophrenia and monitored prospectively for 12 months using standard neuropsychological instruments and the PANSS. Random-effects regression was used to estimate the change over time in neuropsychological function and the association of PANSS covariates. Improvements in positive, negative, and cognitive syndromes were significant predictors of change on nine neuropsychological measures. The neuropsychological function was worsening on five of these measures. The PM model represented the best set of predictors examining positive and negative syndrome covariates, whereas the VDG model consistently represented the best predictors examining cognitive syndrome covariates. The PM positive and negative syndrome factors and the VDG disorganized thoughts syndrome factor are differentially associated with changes in neuropsychological function over time. Clinical investigators may want to target their use of these factors from the PANSS according to the outcome variables being measured.

Modeling the climatic drivers of spatial patterns in vegetation composition since the Last Glacial Maximum

Projecting the future composition and function of communities is a major challenge, and there is an urgent need to develop, improve, and test the predictive capacity of ecological models under different climate states. We tested the effect of climate on spatial patterns of plant community composition over the past 21 000 yr, focusing on whether the spatial relationships between environmental distance and compositional dissimilarity are stable over time. We used a network of fossil‐pollen sites in eastern North America, combined with paleoclimate simulations from the Last Glacial Maximum (LGM; 21 000 calibrated years before present, 21 kyr BP) to the present. We modeled relationships between climate, geography, and compositional dissimilarity at 1 kyr periods using generalized dissimilarity modeling (GDM) and determined the strongest predictors of compositional dissimilarity. We assessed the performance of models calibrated for one time period (e.g. 14 kyr BP) in predicting patterns in the same period as well as at other times (e.g. 12 kyr BP), and tested whether predictive performance was related to the magnitude of climate change between the calibration and prediction time periods. Finally, we examined whether pooling data from multiple time periods improved predictive performance. Models explained 32 to 51% of compositional dissimilarity between locations within any single time period. The best set of predictors changed across time, with summer temperature and geographic distance the strongest predictors of compositional dissimilarity for most time periods. Models built for one time period explained turnover during nearby time periods relatively well, but performance decayed across time and with increasing climate change. Results were similar regardless of whether models were projected forward or backward through time, and did not improve when data were pooled across time. GDM predicts well the spatial patterns of past compositional dissimilarity and holds promise for modeling the drivers of compositional dissimilarity across space and time. However, the modeled relationships between compositional turnover and environmental distance are non‐stationary, so caution is needed when predicting across periods of significant climatic change.

Predictors of Radiographic Knee Osteoarthritis After Anterior Cruciate Ligament Reconstruction

Background: Evidence suggests that single-bundle anterior cruciate ligament (ACL) reconstruction does not reliably prevent the development of knee osteoarthritis (OA). Purpose: This study was conducted to determine the overall prevalence of and risk factors for the development of radiographic knee OA after single-bundle ACL reconstruction. Study Design: Case control study; Level of evidence, 3. Methods: There were 249 individuals who had undergone primary single-bundle ACL reconstruction included in this retrospective cohort study. Follow-up radiographs were scored by a single orthopaedic surgery sports medicine fellow using the Kellgren-Lawrence (KL) scale to determine the degree of OA in the medial, lateral, and patellofemoral compartments. Radiographic OA of the involved knee was considered to be present if, compared with the noninvolved knee, there was at least a 2-grade difference in the KL score in at least 1 compartment or a 1-grade difference in at least 2 compartments. Predictors of OA that were explored included patient age, sex, body mass index (BMI), smoking status activity level, meniscectomy before or concurrent with ACL reconstruction, chondral injury present at the time of ACL reconstruction, graft type and source, tibial and femoral tunnel positions, need for revision, and length of follow-up. Univariable and stepwise multivariable logistic regressions were used to identify factors that were associated with radiographic knee OA. Results: Thirty-nine percent of the patients had radiographic OA an average of 7.8 years after surgery. Female sex, BMI, time from injury to surgery, medial and patellofemoral compartment chondrosis, prior medial or lateral meniscectomy, concurrent medial meniscectomy, and length of follow-up were associated with radiographic knee OA after ACL surgery. Stepwise multivariable logistic regression indicated that prior medial meniscectomy (95% confidence interval [CI], 1.39-6.85), grade 2 or greater medial chondrosis (95% CI, 1.27-6.73), length of follow-up (95% CI, 1.07-1.24), and BMI (overweight 95% CI, 1.08-3.84; obese 95% CI, 1.34-7.80) were the best set of predictors of knee OA. Conclusion: Despite reduced laxity and instability and improved activity and participation, individuals who have undergone ACL reconstruction are still at high risk for developing knee OA compared with the general population. The strongest predictors of knee OA after ACL reconstruction were obesity and grade 2 or greater chondrosis in the medial compartment. These results may aid in identifying patients at risk for OA after ACL reconstruction.

Examining Mutual Elements of the Job Strain Model and the Effort—Reward Imbalance Model Among Special Education Staff in the USA

Two theories of occupational stress are often cited as being most supported by research: the job strain model (JSM) and the effort—reward imbalance model (ERIM). In order to investigate the applicability of mutual theoretical elements of both models to special education in the USA, a sample of 100 special education paraeducators in public and specialized alternative settings was used. Hierarchical regression analysis was conducted to determine the best set of predictors of occupational stress among theoretically mutual components of each model. Results supported the hypothesis that there are common elements of both the JSM and the ERIM that can be predictors of occupational stress among special education personnel in the USA.

Prediction of Dementia in Primary Care Patients

BackgroundCurrent approaches for AD prediction are based on biomarkers, which are however of restricted availability in primary care. AD prediction tools for primary care are therefore needed. We present a prediction score based on information that can be obtained in the primary care setting.Methodology/Principal FindingsWe performed a longitudinal cohort study in 3.055 non-demented individuals above 75 years recruited via primary care chart registries (Study on Aging, Cognition and Dementia, AgeCoDe). After the baseline investigation we performed three follow-up investigations at 18 months intervals with incident dementia as the primary outcome.The best set of predictors was extracted from the baseline variables in one randomly selected half of the sample. This set included age, subjective memory impairment, performance on delayed verbal recall and verbal fluency, on the Mini-Mental-State-Examination, and on an instrumental activities of daily living scale. These variables were aggregated to a prediction score, which achieved a prediction accuracy of 0.84 for AD. The score was applied to the second half of the sample (test cohort). Here, the prediction accuracy was 0.79. With a cut-off of at least 80% sensitivity in the first cohort, 79.6% sensitivity, 66.4% specificity, 14.7% positive predictive value (PPV) and 97.8% negative predictive value of (NPV) for AD were achieved in the test cohort. At a cut-off for a high risk population (5% of individuals with the highest risk score in the first cohort) the PPV for AD was 39.1% (52% for any dementia) in the test cohort.ConclusionsThe prediction score has useful prediction accuracy. It can define individuals (1) sensitively for low cost-low risk interventions, or (2) more specific and with increased PPV for measures of prevention with greater costs or risks. As it is independent of technical aids, it may be used within large scale prevention programs.

Is Persistent Visual Neglect Associated with Poor Survival?

The association of visual neglect with survival after right hemisphere (RH) stroke has received only limited attention. This study explores the relationship of visual neglect and its spontaneous recovery to survival in a homogenous patient group with first-ever RH stroke. Fifty-one RH stroke patients who suffered an infarct between 1994 and 1997 were retrospectively followed for survival until August 31, 2009. Acute-phase neurological, neuropsychological and neuroradiological data were studied to identify predictors of survival. Twenty-eight patients died during the follow-up. Age, education, and poor recovery of visual neglect emerged as significant single predictors of death. The best set of predictors for poor survival in the multivariate model was poor recovery of visual neglect and low education. Poor recovery of visual neglect is associated with long-term mortality in RH infarct patients. The follow-up of RH patients' neuropsychological performance gives additional information about the prognosis.