Are we teaching our students what they need to know about ageing? Results from the National Survey of Undergraduate Teaching in Ageing and Geriatric Medicine

Introduction - Learning about ageing and the appropriate management of older patients is important for all doctors. This survey set out to evaluate what medical undergraduates in the UK are taught about ageing and geriatric medicine and how this teaching is delivered. Methods – An electronic questionnaire was developed and sent to the 28/31 UK medical schools which agreed to participate. Results – Full responses were received from 17 schools. 8/21 learning objectives were recorded as taught, and none were examined, across every school surveyed. Elder abuse and terminology and classification of health were taught in only 8/17 and 2/17 schools respectively. Pressure ulcers were taught about in 14/17 schools but taught formally in only 7 of these and examined in only 9. With regard to bio- and socio- gerontology, only 9/17 schools reported teaching in social ageing, 7/17 in cellular ageing and 9/17 in the physiology of ageing. Discussion – Even allowing for the suboptimal response rate, this study presents significant cause for concern with UK undergraduate education related to ageing. The failure to teach comprehensively on elder abuse and pressure sores, in particular, may be significantly to the detriment of older patients

Analyses of multiplicity distributions with \eta_c and Bose-Einstein correlations at LHC by means of generalized Glauber-Lachs formula

Using the negative binomial distribution (NBD) and the generalized Glauber-Lachs (GGL) formula, we analyze the data on charged multiplicity distributions with pseudo-rapidity cutoffs \eta_c at 0.9, 2.36, and 7 TeV by ALICE Collaboration and at 0.2, 0.54, and 0.9 TeV by UA5 Collaboration. We confirm that the KNO scaling holds among the multiplicity distributions with \eta_c = 0.5 at \sqrt{s} = 0.2\sim2.36 TeV and estimate the energy dependence of a parameter 1/k in NBD and parameters 1/k and \gamma (the ratio of the average value of the coherent hadrons to that of the chaotic hadrons) in the GGL formula. Using empirical formulae for the parameters 1/k and \gamma in the GGL formula, we predict the multiplicity distributions with \eta_c = 0.5 at 7 and 14 TeV. Data on the 2nd order Bose-Einstein correlations (BEC) at 0.9 TeV by ALICE Collaboration and 0.9 and 2.36 TeV by CMS Collaboration are also analyzed based on the GGL formula. Prediction for the 3rd order BEC at 0.9 and 2.36 TeV are presented. Moreover, the information entropy is discussed

Multiplicity dependence of identical particle correlations in the quantum optical approach

Identical particle correlations at fixed multiplicity are consideres in the presence of chaotic and coherent fields. The multiplicity distribution, one-particle momentum density, and two-particle correlation function are obtained based on the diagrammatic representation for cmulants in semi-inclusive events. Our formulation is applied to the analysis of the experimental data on the multiplicity dependence of correlation functions reported by the UA1 and the OPAL Collaborations.Comment: 14 pages, 7 figure

Thermalized Displaced and Squeezed Number States in Coordinate Representation

Within the framework of thermofield dynamics, the wavefunctions of the thermalized displaced number and squeezed number states are given in the coordinate representation. Furthermore, the time evolution of these wavefunctions is considered by introducing a thermal coordinate representation, and we also calculate the corresponding probability densities, average values and variances of position coordinate, which are consistent with results in the literature.Comment: 12 pages, no figures, Revtex. v3: substantially revise

Cross-accent intelligibility of speech in noise: Long-term familiarity and short-term familiarization

Listeners must cope with a great deal of variability in the speech signal, and thus theories of speech perception must also account for variability, which comes from a number of sources, including variation between accents. It is well-known that there is a processing cost when listening to speech in an accent other than one’s own, but recent work has suggested that this cost is reduced when listening to a familiar accent widely represented in the media, and/or when short amounts of exposure to an accent are provided. Little is known, however, about how these factors (long-term familiarity and short-term familiarization with an accent) interact. The current study tested this interaction by playing listeners difficult-to-segment sentences in noise, before and after a familiarization period where the same sentences were heard in the clear, allowing us to manipulate short-term familiarization. Listeners were speakers of either Glasgow English or Standard Southern British English, and they listened to speech in either their own or the other accent, thereby allowing us to manipulate long-term familiarity. Results suggest that both long-term familiarity and short-term familiarization mitigate the perceptual processing costs of listening to an accent that is not one’s own, but seem not to compensate for them entirely, even when the accent is widely heard in the media

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

<p>Abstract</p> <p>Background</p> <p>Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2).</p> <p>Methods</p> <p>A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances.</p> <p>Results</p> <p>The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity.</p> <p>Conclusions</p> <p>Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.</p

Growing old at home – A randomized controlled trial to investigate the effectiveness and cost-effectiveness of preventive home visits to reduce nursing home admissions: study protocol [NCT00644826]

<p>Abstract</p> <p>Background</p> <p>Regarding demographic changes in Germany it can be assumed that the number of elderly and the resulting need for long term care is increasing in the near future. It is not only an individual's interest but also of public concern to avoid a nursing home admission. Current evidence indicates that preventive home visits can be an effective way to reduce the admission rate in this way making it possible for elderly people to stay longer at home than without home visits. As the effectiveness and cost-effectiveness of preventive home visits strongly depends on existing services in the social and health system existing international results cannot be merely transferred to Germany. Therefore it is necessary to investigate the effectiveness and cost-effectiveness of such an intervention in Germany by a randomized controlled trial.</p> <p>Methods</p> <p>The trial is designed as a prospective multi-center randomized controlled trial in the cities of Halle and Leipzig. The trial includes an intervention and a control group. The control group receives usual care. The intervention group receives three additional home visits by non-physician health professionals (1) geriatric assessment, (2) consultation, (3) booster session.</p> <p>The nursing home admission rate after 18 months will be defined as the primary outcome. An absolute risk reduction from a 20% in the control-group to a 7% admission rate in the intervention group including an assumed drop out rate of 30% resulted in a required sample size of N = 320 (n = 160 vs. n = 160).</p> <p>Parallel to the clinical outcome measurement the intervention will be evaluated economically. The economic evaluation will be performed from a society perspective.</p> <p>Discussion</p> <p>To the authors' knowledge for the first time a trial will investigate the effectiveness and cost-effectiveness of preventive home visits for people aged 80 and over in Germany using the design of a randomized controlled trial. Thus, the trial will contribute to the existing evidence on preventive home visits especially in Germany.</p

Predicting nursing home admission in the U.S: a meta-analysis

Background: While existing reviews have identified significant predictors of nursing home admission, this meta-analysis attempted to provide more integrated empirical findings to identify predictors. The present study aimed to generate pooled empirical associations for sociodemographic, functional, cognitive, service use, and informal support indicators that predict nursing home admission among older adults in the U.S. Methods: Studies published in English were retrieved by searching the MEDLINE, PSYCINFO, CINAHL, and Digital Dissertations databases using the keywords: "nursing home placement," "nursing home entry," "nursing home admission," and "predictors/institutionalization." Any reports including these key words were retrieved. Bibliographies of retrieved articles were also searched. Selected studies included sampling frames that were nationally- or regionally-representative of the U.S. older population. Results: Of 736 relevant reports identified, 77 reports across 12 data sources were included that used longitudinal designs and community-based samples. Information on number of nursing home admissions, length of follow-up, sample characteristics, analysis type, statistical adjustment, and potential risk factors were extracted with standardized protocols. Random effects models were used to separately pool the logistic and Cox regression model results from the individual data sources. Among the strongest predictors of nursing home admission were 3 or more activities of daily living dependencies (summary odds ratio [OR] = 3.25; 95% confidence interval [CI], 2.56–4.09), cognitive impairment (OR = 2.54; CI, 1.44–4.51), and prior nursing home use (OR = 3.47; CI, 1.89–6.37). Conclusion: The pooled associations provided detailed empirical information as to which variables emerged as the strongest predictors of NH admission (e.g., 3 or more ADL dependencies, cognitive impairment, prior NH use). These results could be utilized as weights in the construction and validation of prognostic tools to estimate risk for NH entry over a multi-year period

A methodological framework to distinguish spectrum effects from spectrum biases and to assess diagnostic and screening test accuracy for patient populations: Application to the Papanicolaou cervical cancer smear test

<p>Abstract</p> <p>Background</p> <p>A spectrum effect was defined as differences in the sensitivity or specificity of a diagnostic test according to the patient's characteristics or disease features. A spectrum effect can lead to a spectrum bias when subgroup variations in sensitivity or specificity also affect the likelihood ratios and thus post-test probabilities. We propose and illustrate a methodological framework to distinguish spectrum effects from spectrum biases.</p> <p>Methods</p> <p>Data were collected for 1781 women having had a cervical smear test and colposcopy followed by biopsy if abnormalities were detected (the reference standard). Logistic models were constructed to evaluate both the sensitivity and specificity, and the likelihood ratios, of the test and to identify factors independently affecting the test's characteristics.</p> <p>Results</p> <p>For both tests, human papillomavirus test, study setting and age affected sensitivity or specificity of the smear test (spectrum effect), but only human papillomavirus test and study setting modified the likelihood ratios (spectrum bias) for clinical reading, whereas only human papillomavirus test and age modified the likelihood ratios (spectrum bias) for "optimized" interpretation.</p> <p>Conclusion</p> <p>Fitting sensitivity, specificity and likelihood ratios simultaneously allows the identification of covariates that independently affect diagnostic or screening test results and distinguishes spectrum effect from spectrum bias. We recommend this approach for the development of new tests, and for reporting test accuracy for different patient populations.</p