33 resultados para Maximum-entropy probability density
Resumo:
In a system where tens of thousands of words are made up of a limited number of phonemes, many words are bound to sound alike. This similarity of the words in the lexicon as characterized by phonological neighbourhood density (PhND) has been shown to affect speed and accuracy of word comprehension and production. Whereas there is a consensus about the interfering nature of neighbourhood effects in comprehension, the language production literature offers a more contradictory picture with mainly facilitatory but also interfering effects reported on word production. Here we report both of these two types of effects in the same study. Multiple regression mixed models analyses were conducted on PhND effects on errors produced in a naming task by a group of 21 participants with aphasia. These participants produced more formal errors (interfering effect) for words in dense phonological neighbourhoods, but produced fewer nonwords and semantic errors (a facilitatory effect) with increasing density. In order to investigate the nature of these opposite effects of PhND, we further analysed a subset of formal errors and nonword errors by distinguishing errors differing on a single phoneme from the target (corresponding to the definition of phonological neighbours) from those differing on two or more phonemes. This analysis confirmed that only formal errors that were phonological neighbours of the target increased in dense neighbourhoods, while all other errors decreased. Based on additional observations favouring a lexical origin of these formal errors (they exceeded the probability of producing a real-word error by chance, were of a higher frequency, and preserved the grammatical category of the targets), we suggest that the interfering effect of PhND is due to competition between lexical neighbours and target words in dense neighbourhoods.
Resumo:
Tools to predict fracture risk are useful for selecting patients for pharmacological therapy in order to reduce fracture risk and redirect limited healthcare resources to those who are most likely to benefit. FRAX® is a World Health Organization fracture risk assessment algorithm for estimating the 10-year probability of hip fracture and major osteoporotic fracture. Effective application of FRAX® in clinical practice requires a thorough understanding of its limitations as well as its utility. For some patients, FRAX® may underestimate or overestimate fracture risk. In order to address some of the common issues encountered with the use of FRAX® for individual patients, the International Society for Clinical Densitometry (ISCD) and International Osteoporosis Foundation (IOF) assigned task forces to review the medical evidence and make recommendations for optimal use of FRAX® in clinical practice. Among the issues addressed were the use of bone mineral density (BMD) measurements at skeletal sites other than the femoral neck, the use of technologies other than dual-energy X-ray absorptiometry, the use of FRAX® without BMD input, the use of FRAX® to monitor treatment, and the addition of the rate of bone loss as a clinical risk factor for FRAX®. The evidence and recommendations were presented to a panel of experts at the Joint ISCD-IOF FRAX® Position Development Conference, resulting in the development of Joint ISCD-IOF Official Positions addressing FRAX®-related issues.
Resumo:
This study aimed to develop a hip screening tool that combines relevant clinical risk factors (CRFs) and quantitative ultrasound (QUS) at the heel to determine the 10-yr probability of hip fractures in elderly women. The EPISEM database, comprised of approximately 13,000 women 70 yr of age, was derived from two population-based white European cohorts in France and Switzerland. All women had baseline data on CRFs and a baseline measurement of the stiffness index (SI) derived from QUS at the heel. Women were followed prospectively to identify incident fractures. Multivariate analysis was performed to determine the CRFs that contributed significantly to hip fracture risk, and these were used to generate a CRF score. Gradients of risk (GR; RR/SD change) and areas under receiver operating characteristic curves (AUC) were calculated for the CRF score, SI, and a score combining both. The 10-yr probability of hip fracture was computed for the combined model. Three hundred seven hip fractures were observed over a mean follow-up of 3.2 yr. In addition to SI, significant CRFs for hip fracture were body mass index (BMI), history of fracture, an impaired chair test, history of a recent fall, current cigarette smoking, and diabetes mellitus. The average GR for hip fracture was 2.10 per SD with the combined SI + CRF score compared with a GR of 1.77 with SI alone and of 1.52 with the CRF score alone. Thus, the use of CRFs enhanced the predictive value of SI alone. For example, in a woman 80 yr of age, the presence of two to four CRFs increased the probability of hip fracture from 16.9% to 26.6% and from 52.6% to 70.5% for SI Z-scores of +2 and -3, respectively. The combined use of CRFs and QUS SI is a promising tool to assess hip fracture probability in elderly women, especially when access to DXA is limited.