Miracle-dz/UniFAR-FTUs huggingface dataset:
Achieving Electric-Acoustic Benefit with a Modulated Tone

Objective: When either real or simulated electric stimulation from a cochlear implant (CI) is combined with low-frequency acoustic stimulation (electric-acoustic stimulation [EAS]), speech intelligibility in noise can improve dramatically. We recently showed that a similar benefit to intelligibility can be observed in simulation when the low-frequency acoustic stimulation (low-pass target speech) is replaced with a tone that is modulated both in frequency with the fundamental frequency (F0) of the target talker and in amplitude with the amplitude envelope of the low-pass target speech (Brown & Bacon 2009). The goal of the current experiment was to examine the benefit of the modulated tone to intelligibility in CI patients. Design: Eight CI users who had some residual acoustic hearing either in the implanted ear, the unimplanted ear, or both ears participated in this study. Target speech was combined with either multitalker babble or a single competing talker and presented to the implant. Stimulation to the acoustic region consisted of no signal, target speech, or a tone that was modulated in frequency to track the changes in the target talker's F0 and in amplitude to track the amplitude envelope of target speech low-pass filtered at 500 Hz. Results: All patients showed improvements in intelligibility over electric-only stimulation when either the tone or target speech was presented acoustically. The average improvement in intelligibility was 46 percentage points due to the tone and 55 percentage points due to target speech. Conclusions: The results demonstrate that a tone carrying F0 and amplitude envelope cues of target speech can provide significant benefit to CI users and may lead to new technologies that could offer EAS benefit to many patients who would not benefit from current EAS approaches.
Auditory N1 Component to Gaps in Continuous Narrowband Noises

Objectives: To determine whether (1) the auditory N1 component can be elicited to gaps in continuous narrowband noises, (2) psychophysical and electrophysiological gap thresholds (PGTs and EGTs) are similar to one another, and (3) EGTs are the same for all narrowband noise center frequencies. Design: PGTs and EGTs were obtained from 18 normal-hearing young-adult listeners to gaps in continuous narrowband noises with center frequencies of 0.5, 1, or 4 kHz. PGTs were obtained with a modified Békésy-type tracking paradigm, whereas EGTs were obtained to 2-, 5-, 10-, 20-, or 50-msec gaps presented every 2.2 sec. Results: (1) The auditory N1 component was recorded to gaps in narrowband noises, although they appeared morphologically different from cortical potentials obtained using the continuous broadband noise. (2) At center frequencies of 1 and 4 kHz, psychometric functions revealed close similarity between PGTs and EGTs. However, different results were present for the 0.5-kHz narrowband noise, attributed to stimulus artifact. (3) EGTs were approximately 10 msec for most participants at 1 and 4 kHz, but 20 msec at 0.5 kHz, corroborating other studies showing increases in gap threshold with lower center frequencies. Conclusions: The auditory N1 component can be recorded to gaps in continuous narrowband noises whose gap thresholds are grossly similar to those obtained psychophysically. The differences found between PGTs and EGTs with different narrowband noise center frequencies call for further investigation of narrowband noise stimuli for the study of temporal resolution.
Inmigración, lactancia materna y hábito tabáquico

Objetivo Los beneficios de la lactancia materna para los ninos y las madres estan generalmente reconocidos. Muchos factores influyen en el inicio de la lactancia materna. Entre ellos se ha senalado el estatus de inmigrantes de los padres. Algunos estudios han senalado elevadas tasas de lactancia materna entre la poblacion inmigrante. El presente trabajo estudia la lactancia materna en poblacion espanola e inmigrante, y su relacion con otros factores del periodo neonatal. Metodos Se han estudiado 911 ninos durante un periodo de 10 meses, se recogieron datos sobre edad, sexo, duracion del embarazo, peso al nacimiento, edad y habito tabaquico de los padres durante el embarazo, y su relacion con el inicio de lactancia materna y su duracion (odds ratio [OR] e intervalo de confianza [IC] del 95 %) en los grupos de poblacion inmigrante y autoctona, realizandose tambien analisis multivariante incluyendo todas las variables estudiadas. Resultados Las variables estudiadas fueron similares entre la poblacion autoctona y la inmigrante excepto la edad materna y el habito tabaquico. En el analisis multivariante solo presentaron significacion estadistica con la lactancia materna el habito tabaquico de la madre (inicio: OR 0,610 [IC 95%: 0,419-0,867]; a los 3 meses: OR 0,540 [IC 95%: 0,458-0,636] y el peso al nacimiento Conclusion Nuestros datos senalan que los factores de riesgo para el no inicio de la lactancia materna o su no mantenimiento a los 3 meses son el bajo peso al nacimiento y el tabaquismo materno. El ser inmigrante se ha comportado como un factor de confusion.
Preference for One or Two Hearing Aids Among Adult Patients

Objectives: Most practitioners believe that use of two hearing aids is the ideal fitting for adults with bilateral symmetrical hearing loss. However, previous research has consistently shown that a substantial proportion of these patients actually prefer to use only one hearing aid. This study explored whether this pattern of preferences is seen with technologically advanced hearing aids. In addition, a selection of variables that were available prefitting were used to attempt to predict which patients will prefer one hearing aid rather than two. Design: The study was designed as a 12-week field trial including structured and unstructured use of one and two hearing aids. Ninety-four subjects with mild to moderate bilaterally symmetrical hearing loss were bilaterally fit with 2005–2007 era hearing aids. Potential predictors included demographic, audiometric, auditory lifestyle, personality, and binaural processing variables. After the field trial, each subject stated his or her preference for one or two hearing aids and completed three self-report outcome questionnaires for their preferred fitting. Results: Previous research was confirmed with modern technology hearing aids: after the field trial, 46% of the subjects preferred to use one hearing aid rather than two. Subjects who preferred two hearing aids tended to report better real-world outcomes than those who preferred one. Subjects who reported more hearing problems in daily life, who experienced more binaural loudness summation, and whose ears were more equivalent in dichotic listening were more likely to prefer to use two hearing aids. Contrary to conventional wisdom (ideas that are generally accepted as true), audiometric hearing loss and auditory lifestyle were not predictive of aiding preference. However, the best predictive approach from these data yielded accurate predictions for only two-thirds of the subjects. Conclusions: Evidence-based practice calls for a conscientious melding of current evidence, clinical judgment, and patient preferences. The results of this research challenge practitioners to recognize that many patients who seem to be ideal candidates for bilateral aiding will actually prefer to wear only one hearing aid. Furthermore, at this time, there is no accurate method that will predict which patients will prefer one hearing aid rather than two. At present, the most effective approach open to practitioners would be to conduct a candid unbiased systematic field trial allowing each patient to compare unilateral and bilateral fittings in daily life. This might necessitate more fitting sessions and could perhaps add to the practitioner's burden. This downside should be weighed against the additional patient satisfaction that can be anticipated as a result of transparency in the fitting protocol, collaboration with the patient in the treatment decisions, and the knowledge of selecting the most cost-effective patient-centered solution.
On measures of dissimilarity between point patterns: Classification based on prototypes and multidimensional scaling

This paper presents a collection of dissimilarity measures to describe and then classify spatial point patterns when multiple replicates of different types are available for analysis. In particular, we consider a range of distances including the spike‐time distance and its variants, as well as cluster‐based distances and dissimilarity measures based on classical statistical summaries of point patterns. We review and explore, in the form of a tutorial, their uses, and their pros and cons. These distances are then used to summarize and describe collections of repeated realizations of point patterns via prototypes and multidimensional scaling. We also show a simulation study to evaluate the performance of multidimensional scaling with two types of selected distances. Finally, a multivariate spatial point pattern of a natural plant community is analyzed through various of these measures of dissimilarity.
Using conditional power of network meta‐analysis (NMA) to inform the design of future clinical trials

Clinical trials are typically designed with an aim to reach sufficient power to test a hypothesis about relative effectiveness of two or more interventions. Their role in informing evidence‐based decision‐making demands, however, that they are considered in the context of the existing evidence. Consequently, their planning can be informed by characteristics of relevant systematic reviews and meta‐analyses. In the presence of multiple competing interventions the evidence base has the form of a network of trials, which provides information not only about the required sample size but also about the interventions that should be compared in a future trial. In this paper we present a methodology to evaluate the impact of new studies, their information size, the comparisons involved, and the anticipated heterogeneity on the conditional power (CP) of the updated network meta‐analysis. The methods presented are an extension of the idea of CP initially suggested for a pairwise meta‐analysis and we show how to estimate the required sample size using various combinations of direct and indirect evidence in future trials. We apply the methods to two previously published networks and we show that CP for a treatment comparison is dependent on the magnitude of heterogeneity and the ratio of direct to indirect information in existing and future trials for that comparison. Our methodology can help investigators calculate the required sample size under different assumptions about heterogeneity and make decisions about the number and design of future studies (set of treatments compared).
Relationship Between Auditory Function of Nonimplanted Ears and Bimodal Benefit

Objectives: Patients with a cochlear implant (CI) in one ear and a hearing aid in the other ear commonly achieve the highest speech-understanding scores when they have access to both electrically and acoustically stimulated information. At issue in this study was whether a measure of auditory function in the hearing aided ear would predict the benefit to speech understanding when the information from the aided ear was added to the information from the CI. Design: The subjects were 22 bimodal listeners with a CI in one ear and low-frequency acoustic hearing in the nonimplanted ear. The subjects were divided into two groups—one with mild-to-moderate low-frequency loss and one with severe-to-profound loss. Measures of auditory function included (1) audiometric thresholds at 750 Hz or lower, (2) speech-understanding scores (words in quiet and sentences in noise), and (3) spectral-modulation detection (SMD) thresholds. In the SMD task, one stimulus was a flat spectrum noise and the other was a noise with sinusoidal modulations at 1.0 peak/octave. Results: Significant correlations were found among all three measures of auditory function and the benefit to speech understanding when the acoustic and electric stimulation were combined. Benefit was significantly correlated with audiometric thresholds (r = −0.814), acoustic speech understanding (r = 0.635), and SMD thresholds (r = −0.895) in the hearing aided ear. However, only the SMD threshold was significantly correlated with benefit within the group with mild-to-moderate loss (r = −0.828) and within the group with severe-to-profound loss (r = −0.896). Conclusions: The SMD threshold at 1 cycle/octave has the potential to provide clinicians with information relevant to the question of whether an ear with low-frequency hearing is likely to add to the intelligibility of speech provided by a CI.
Nonidentical twins: Comparison of frequentist and Bayesian lasso for Cox models

One important task in translational cancer research is the search for new prognostic biomarkers to improve survival prognosis for patients. The use of high‐throughput technologies allows simultaneous measurement of genome‐wide gene expression or other genomic data for all patients in a clinical trial. Penalized likelihood methods such as lasso regression can be applied to such high‐dimensional data, where the number of (genomic) covariables is usually much larger than the sample size. There is a connection between the lasso and the Bayesian regression model with independent Laplace priors on the regression parameters, and understanding this connection has been useful for understanding the properties of lasso estimates in linear models (e.g. Park and Casella, ). In this paper, we study the lasso in the frequentist and Bayesian frameworks in the context of Cox models. For the Bayesian lasso we extend the approach by Lee et al. ( ). In particular, we impose the lasso penalty only on the genome features, but not on relevant clinical covariates, to allow the mandatory inclusion of important established factors. We investigate the models in high‐ and low‐dimensional simulation settings and in an application to chronic lymphocytic leukemia.
Bayesian model selection in logistic regression for the detection of adverse drug reactions

Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, the analysis of such databases requires statistical methods. In this context, disproportionality measures can be used. Their main idea is to project the data onto contingency tables in order to measure the strength of associations between drugs and adverse events. However, due to the data projection, these methods are sensitive to the problem of coprescriptions and masking effects. Recently, logistic regressions have been used with a Lasso type penalty to perform the detection of associations between drugs and adverse events. On different examples, this approach limits the drawbacks of the disproportionality methods, but the choice of the penalty value is open to criticism while it strongly influences the results. In this paper, we propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis–Hastings algorithm carries out the model selection by maximizing the BIC criterion. Thus, we avoid the calibration of penalty or threshold. During our application on the French pharmacovigilance database, the proposed method is compared to well‐established approaches on a reference dataset, and obtains better rates of positive and negative controls. However, many signals (i.e., specific drug–event associations) are not detected by the proposed method. So, we conclude that this method should be used in parallel to existing measures in pharmacovigilance.
Identification of subpopulations with distinct treatment benefit rate using the Bayesian tree

Characterization of a subpopulation by the difference in marginal means of the outcome under the intervention and control may not be sufficient to provide informative guidance for individual decision and public policy making. Specifically, often we are interested in the treatment benefit rate (TBR), that is, the probability of benefitting an intervention in a meaningful way. For binary outcomes, TBR is the proportion that has “unfavorable” outcome under the control and “favorable” outcome under the intervention. Identification of subpopulations with distinct TBR by baseline characteristics will have significant implications in clinical setting where a medical intervention with potential negative health impact is under consideration for a given patient. In addition, these subpopulations with unique TBR set the basis for guidance in implementing the intervention toward a more personalized scheme of treatment. In this article, we propose a Bayesian tree based latent variable model to seek subpopulations with distinct TBR. Our method offers a nonparametric Bayesian framework that accounts for the uncertainty in estimating potential outcomes and allows more exhaustive search of the partitions of the baseline covariates space. The method is evaluated through a simulation study and applied to a randomized clinical trial of implantable cardioverter defibrillators to reduce mortality.
Redefining the Outcomes to Resources Ratio for Burn Patient Triage in a Mass Casualty

Recent disasters highlight the need for predisaster planning, including the need for accurate triage. Data-driven triage tables, such as that generated from the 2002 National Burn Repository, are vital to optimize resource use during a disaster. The study purpose was to generate a burn resource disaster triage table based on current burn-treatment outcomes. Data from the NBR after the year 2000 were audited. Records that missed age, burn size, or survival status were excluded from analysis. Duplicate records, readmissions, transfers, and nonburn injuries were eliminated. Resource use was divided into expectant (predicted mortality >90%), low (mortality 50–90%), medium (mortality 10–50%), high (mortality <10%, admission 14–21 days), very high (mortality <10%, admission <14 days), and outpatient. Tables were created for all patient admissions and with/without inhalation injury. Of the 286,293 records, 210,683 were from the year 2000 or later. Expectant status for those aged >70 years began at 50% burn; a 20- to 29-year-old never reached expectant status. Inhalation injury lowered the expectant category to a burn size of 40% in >70-year-olds, and at >90% in 20- to 29-year-olds. The 0- to 1.9-year old group without inhalation injury never reached expectant status; with inhalation injury, expectant status was reached at >80% burn. Changes in the triage tables suggest that burn care has changed in the past 10 years. Inhalation injury significantly alters triage in a burn disaster. Use of these updated tables for triage in a disaster may improve our ability to allocate resources.
A Health Services Use Framework for Drug-Abusing Offenders

This article reviews the recent literature on health services use by drug abusers. A revised Andersen-Newman model is posited as a theoretical orientation for health service initiatives focused on drug abuse. The model highlights specific predisposing factors, historical health factors, current illness factors, and enabling/mediating factors associated with drug abuse. The limited number of cost-effectiveness studies have demonstrated that publicly funded drug treatment can produce savings by reducing selected health care costs and decreasing crime. However, short-term costs related to drug abuse can be greater to realize than long-term savings. The criminal justice system can serve as a point of contact and provides a concentrated reservoir of drug users. However, drug-abusing offenders are more likely to present a variety of health problems including hepatitis, tuberculosis, and HIV/AIDS. From a managed care and health services use perspective, drug abuse treatment in the criminal justice system should minimize long-term health care- and crime-related costs.
Multivariate binary classification of imbalanced datasets—A case study based on high‐dimensional multiplex autoimmune assay data

The classification of a population by a specific trait is a major task in medicine, for example when in a diagnostic setting groups of patients with specific diseases are identified, but also when in predictive medicine a group of patients is classified into specific disease severity classes that might profit from different treatments. When the sizes of those subgroups become small, for example in rare diseases, imbalances between the classes are more the rule than the exception and make statistical classification problematic when the error rate of the minority class is high. Many observations are classified as belonging to the majority class, while the error rate of the majority class is low. This case study aims to investigate class imbalance for Random Forests and Powered Partial Least Squares Discriminant Analysis (PPLS‐DA) and to evaluate the performance of these classifiers when they are combined with methods to compensate imbalance (sampling methods, cost‐sensitive learning approaches). We evaluate all approaches with a scoring system taking the classification results into consideration. This case study is based on one high‐dimensional multiplex autoimmune assay dataset describing immune response to antigens and consisting of two classes of patients: Rheumatoid Arthritis (RA) and Systemic Lupus Erythemathodes (SLE). Datasets with varying degrees of imbalance are created by successively reducing the class of RA patients. Our results indicate possible benefit of cost‐sensitive learning approaches for Random Forests. Although further research is needed to verify our findings by investigating other datasets or large‐scale simulation studies, we claim that this work has the potential to increase awareness of practitioners to this problem of class imbalance and stresses the importance of considering methods to compensate class imbalance.
Adaptively distributing cognition: A decision-making perspective on human - computer interaction

Two important phenomena in human - computer interaction (HCI) are considered: the reliance on external information rather than memory, and the interleaving of planning and action. These phenomena are important, it is argued, because they challenge some particular cognitive models. However, we reject those views, influential in the HCI literature, that phenomena like these require radically new conceptions of cognition or behaviour. It is shown that the phenomena are not universal laws of behaviour, but that instead people decide how much to remember and how much to plan according to a consideration of the costs and benefits of different strategies. Thus the classical cognitive conception of humans as adaptive decision makers is vital for a deep understanding of HCI.