Construct-level predictive validity of educational attainment and intellectual aptitude tests in medical student selection: meta-regression of six UK longitudinal studies

Background: Measures used for medical student selection should predict future performance during training. A problem for any selection study is that predictor-outcome correlations are known only in those who have been selected, whereas selectors need to know how measures would predict in the entire pool of applicants. That problem of interpretation can be solved by calculating construct-level predictive validity, an estimate of true predictor-outcome correlation across the range of applicant abilities. Methods: Construct-level predictive validities were calculated in six cohort studies of medical student selection and training (student entry, 1972 to 2009) for a range of predictors, including A-levels, General Certificates of Secondary Education (GCSEs)/O-levels, and aptitude tests (AH5 and UK Clinical Aptitude Test (UKCAT)). Outcomes included undergraduate basic medical science and finals assessments, as well as postgraduate measures of Membership of the Royal Colleges of Physicians of the United Kingdom (MRCP(UK)) performance and entry in the Specialist Register. Construct-level predictive validity was calculated with the method of Hunter, Schmidt and Le (2006), adapted to correct for right-censorship of examination results due to grade inflation. Results: Meta-regression analyzed 57 separate predictor-outcome correlations (POCs) and construct-level predictive validities (CLPVs). Mean CLPVs are substantially higher (.450) than mean POCs (.171). Mean CLPVs for first-year examinations, were high for A-levels (.809; CI: .501 to .935), and lower for GCSEs/O-levels (.332; CI: .024 to .583) and UKCAT (mean = .245; CI: .207 to .276). A-levels had higher CLPVs for all undergraduate and postgraduate assessments than did GCSEs/O-levels and intellectual aptitude tests. CLPVs of educational attainment measures decline somewhat during training, but continue to predict postgraduate performance. Intellectual aptitude tests have lower CLPVs than A-levels or GCSEs/O-levels. Conclusions: Educational attainment has strong CLPVs for undergraduate and postgraduate performance, accounting for perhaps 65% of true variance in first year performance. Such CLPVs justify the use of educational attainment measure in selection, but also raise a key theoretical question concerning the remaining 35% of variance (and measurement error, range restriction and right-censorship have been taken into account). Just as in astrophysics, ‘dark matter’ and ‘dark energy’ are posited to balance various theoretical equations, so medical student selection must also have its ‘dark variance’, whose nature is not yet properly characterized, but explains a third of the variation in performance during training. Some variance probably relates to factors which are unpredictable at selection, such as illness or other life events, but some is probably also associated with factors such as personality, motivation or study skills

Shear-free, Irrotational, Geodesic, Anisotropic Fluid Cosmologies

General relativistic anisotropic fluid models whose fluid flow lines form a shear-free, irrotational, geodesic timelike congruence are examined. These models are of Petrov type D, and are assumed to have zero heat flux and an anisotropic stress tensor that possesses two distinct non-zero eigenvalues. Some general results concerning the form of the metric and the stress-tensor for these models are established. Furthermore, if the energy density and the isotropic pressure, as measured by a comoving observer, satisfy an equation of state of the form $p = p(\mu)$, with $\frac{dp}{d\mu} \neq -\frac{1}{3}$, then these spacetimes admit a foliation by spacelike hypersurfaces of constant Ricci scalar. In addition, models for which both the energy density and the anisotropic pressures only depend on time are investigated; both spatially homogeneous and spatially inhomogeneous models are found. A classification of these models is undertaken. Also, a particular class of anisotropic fluid models which are simple generalizations of the homogeneous isotropic cosmological models is studied.Comment: 13 pages LaTe

Predictive validity of the UK clinical aptitude test in the final years of medical school:a prospective cohort study

Peer reviewedPublisher PD

Application of neural networks in modelling serviceability deterioration of concrete stormwater pipes

Stormwater pipe systems in Australia are designed to convey water from rainfall and surface runoff only and do not transport sewage. Any blockage can cause flooding events with the probability of subsequent property damage. Proactive maintenance plans that can enhance their serviceability need to be developed based on a sound deterioration model. This paper uses a neural network (NN) approach to model deterioration in serviceability of concrete stormwater pipes, which make up the bulk of the stormwater network in Australia. System condition data was collected using CCTV images. The outcomes of model are the identification of the significant factors influencing the serviceability deterioration and the forecasting of the change of serviceability condition over time for individual pipes based on the pipe attributes. The proposed method is validated and compared with multiple discriminant analysis, a traditionally statistical method. The results show that the NN model can be applied to forecasting serviceability deterioration. However, further improvements in data collection and condition grading schemes should be carried out to increase the prediction accuracy of the NN model.<br /

Very long optical path-length from a compact multi-pass cell

The multiple-pass optical cell is an important tool for laser absorption spectroscopy and its many applications. For most practical applications, such as trace-gas detection, a compact and robust design is essential. Here we report an investigation into a multi-pass cell design based on a pair of cylindrical mirrors, with a particular focus on achieving very long optical paths. We demonstrate a path-length of 50.31 m in a cell with 40 mm diameter mirrors spaced 88.9 mm apart - a 3-fold increase over the previously reported longest path-length obtained with this type of cell configuration. We characterize the mechanical stability of the cell and describe the practical conditions necessary to achieve very long path-lengths

Increase in the proportion of patients hospitalized with acute myocardial infarction with do-not-resuscitate orders already in place between 2001 and 2007: a nonconcurrent prospective study

BACKGROUND AND OBJECTIVE: Shared decision making and advance planning in end-of-life decisions have become increasingly important aspects of the management of seriously ill patients. Here, we describe the use and timing of do-not-resuscitate (DNR) orders in patients hospitalized with acute myocardial infarction (AMI). STUDY DESIGN AND SETTING: The nonconcurrent prospective study population consisted of 4182 patients hospitalized with AMI in central Massachusetts in four annual periods between 2001 and 2007. RESULTS: One-quarter (25%) of patients had a DNR order written either prior to or during hospitalization. The frequency of DNR orders remained constant (24% in 2001; 26% in 2007). Among patients with DNR orders, there was a significant increase in orders written prior to hospitalization (2001: 9%; 2007: 55%). Older patients and those with a medical history of heart failure or myocardial infarction were more likely to have prior DNR orders than respective comparison groups. Patients with prior DNR orders were less likely to die 1 month after hospitalization than patients whose DNRs were written during hospitalization. CONCLUSION: Although the use of DNR orders in patients hospitalized with AMI was stable during the period under study, in more recent years, patients are increasingly being hospitalized with DNR orders already in place

In-hospital Depression Predicts Early Hospital Readmission after an Acute Coronary Syndrome: Preliminary Data from TRACE-CORE

Background: Hospital systems, patients and providers seek to avert rehospitalizations within 30 days for patients admitted with an acute coronary syndrome (ACS). Rehospitalizations within 30 days of discharge are often considered preventable and to reflect poor in-hospital management or discharge practices. However, independent associations of psychosocial factors with early rehospitalization in patients admitted with an ACS have not been examined. Methods: A multi-racial cohort of 1,540 patients admitted with an ACS reported psychosocial factors via standardized questionnaires in an in-hospital interview. One month following discharge, patients were interviewed via phone and reported hospital readmissions. We used logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) of the association between in-hospital psychosocial characteristics (depression, anxiety, and perceived stress), health literacy and numeracy, and cognitive status, with self-reported readmission within 30 days. Results: Participants were 34% female and 17% non-white, with a mean age of 62 years and a mean length of stay of 4.1 days. Rehospitalization was reported for 14% (n=208) of participants, 77% of which were due to CVD. In univariate analyses, in-hospital severe depression, anxiety, and high stress were associated with higher odds of early readmission, whereas low health numeracy was associated with lower odds of early readmission. Severe depression remained associated with higher odds and low health numeracy remained associated with lower odds of early readmission in a multivariable model including covariates associated on univariate testing with rehospitalization. Conclusions: Early readmission after hospitalization for an ACS was common and associated with in-hospital depression and health numeracy. Notably, depression and health numeracy were the only predictors independently associated with readmission in multivariable analyses. We speculate that the lower likelihood of readmission for those with low numeracy may be related to less engagement with the healthcare system. In-hospital screening for depression and characterization of health numeracy may help stratify risk for early rehospitalization after an ACS

Mass fluxes and isofluxes of methane (CH4) at a New Hampshire fen measured by a continuous wave quantum cascade laser spectrometer

We have developed a mid‐infrared continuous‐wave quantum cascade laser direct‐absorption spectrometer (QCLS) capable of high frequency (≥1 Hz) measurements of 12CH4 and 13CH4 isotopologues of methane (CH4) with in situ 1‐s RMS image precision of 1.5 ‰ and Allan‐minimum precision of 0.2 ‰. We deployed this QCLS in a well‐studied New Hampshire fen to compare measurements of CH4 isoflux by eddy covariance (EC) to Keeling regressions of data from automated flux chamber sampling. Mean CH4 fluxes of 6.5 ± 0.7 mg CH4 m−2 hr−1 over two days of EC sampling in July, 2009 were indistinguishable from mean autochamber CH4 fluxes (6.6 ± 0.8 mgCH4 m−2 hr−1) over the same period. Mean image composition of emitted CH4 calculated using EC isoflux methods was −71 ± 8 ‰ (95% C.I.) while Keeling regressions of 332 chamber closing events over 8 days yielded a corresponding value of −64.5 ± 0.8 ‰. Ebullitive fluxes, representing ∼10% of total CH4 fluxes at this site, were on average 1.2 ‰ enriched in 13C compared to diffusive fluxes. CH4 isoflux time series have the potential to improve process‐based understanding of methanogenesis, fully characterize source isotopic distributions, and serve as additional constraints for both regional and global CH4 modeling analysis

Atrial Fibrillation Prediction from Critically Ill Sepsis Patients

Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients\u27 AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices