Effects of Tonality, Contour, Pitch Intervals, and Hemisphere on the Representation of Melodic Information

Tonality, contour, interval, and hemisphere are important predictors of melody recognition. Using forced-choice comparisons, listeners attempted to recognize the contour and interval information for diatonic and nondiatonic melodies presented to the left or right ear. For diatonic melodies, scale was more salient than contour whereas listeners relied on contour in nondiatonic melodies

Prevalence of Invalid Performance on Baseline Testing for Sport-Related Concussion by Age and Validity Indicator

Importance: Estimated base rates of invalid performance on baseline testing (base rates of failure) for the management of sport-related concussion range from 6.1% to 40.0%, depending on the validity indicator used. The instability of this key measure represents a challenge in the clinical interpretation of test results that could undermine the utility of baseline testing. Objectives: To determine the prevalence of invalid performance on baseline testing and to assess whether the prevalence varies as a function of age and validity indicator. Design, Setting, and Participants: This retrospective, cross-sectional study included data collected between January 1, 2012, and December 31, 2016, from a clinical referral center in the Midwestern United States. Participants included 7897 consecutively tested, equivalently proportioned male and female athletes aged 10 to 21 years, who completed baseline neurocognitive testing for the purpose of concussion management. Interventions: Baseline assessment was conducted with the Immediate Postconcussion Assessment and Cognitive Testing (ImPACT), a computerized neurocognitive test designed for assessment of concussion. Main Outcomes and Measures: Base rates of failure on published ImPACT validity indicators were compared within and across age groups. Hypotheses were developed after data collection but prior to analyses. Results: Of the 7897 study participants, 4086 (51.7%) were male, mean (SD) age was 14.71 (1.78) years, 7820 (99.0%) were primarily English speaking, and the mean (SD) educational level was 8.79 (1.68) years. The base rate of failure ranged from 6.4% to 47.6% across individual indicators. Most of the sample (55.7%) failed at least 1 of 4 validity indicators. The base rate of failure varied considerably across age groups (117 of 140 [83.6%] for those aged 10 years to 14 of 48 [29.2%] for those aged 21 years), representing a risk ratio of 2.86 (95% CI, 2.60-3.16; P \u3c .001). Conclusions and Relevance: The results for base rate of failure were surprisingly high overall and varied widely depending on the specific validity indicator and the age of the examinee. The strong age association, with 3 of 4 participants aged 10 to 12 years failing validity indicators, suggests that the clinical interpretation and utility of baseline testing in this age group is questionable. These findings underscore the need for close scrutiny of performance validity indicators on baseline testing across age groups

The Post-Concussion Symptom Scale: Normative Data for Adolescent Student-Athletes Stratified by Gender and Preexisting Conditions

BACKGROUND: The Post-Concussion Symptom Scale (PCSS) is a self-report questionnaire measuring symptoms that commonly occur after a concussion; however, these symptoms are nonspecific and can be related to co-occurring orthopaedic injuries (eg, cervical strain) or patient characteristics and preexisting conditions, even in the absence of a recent injury. As such, clinicians may have difficulty determining whether symptom elevations are attributable to a recent concussion as opposed to a confounding injury or a preexisting condition, which may be especially difficult when preinjury baseline symptom data are unavailable. PURPOSE: This study aimed to further validate the 4-factor model of the PCSS (ie, cognitive, sleep-arousal, physical, and affective symptoms) with adolescent student-athletes and provide normative reference data for each factor and the total score, stratified by gender and preexisting health conditions. STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: Participants were 9358 adolescent student-athletes who completed the PCSS during a preseason baseline evaluation (mean age, 14.9 years; SD, 1.3 years [range, 13-18 years]; 49.3% boys). The 4-factor model of the PCSS was tested for the full sample and separately for boys and girls using confirmatory factor analysis. Symptom severity percentiles were created for the PCSS total score and each factor, stratified by gender and preexisting conditions (ie, attention-deficit/hyperactivity disorder, mental health history, headache/migraine history, learning disability/dyslexia, academic problems, and concussion history). RESULTS: The 4-factor model of the PCSS replicated in the full sample (comparative fit index [CFI] = 0.959) and in both gender groups (boys: CFI = 0.961; girls: CFI = 0.960). The total PCSS score at the 84th percentile varied by preexisting conditions as follows: healthy participants = 8, attention-deficit/hyperactivity disorder = 18, mental health history = 26, headache/migraine history = 18, learning disability = 19, and academic problems = 17. On all PCSS subscales, participants with a mental health history had the highest scores, and high scores were associated with having \u3e1 preexisting condition. Girls had higher scores than boys for each stratification. CONCLUSION: The 4-factor model of the PCSS replicates for adolescent student-athletes. Gender, number of preexisting conditions, and mental health history are important factors to account for when interpreting PCSS symptom severity. The normative data provided herein could assist clinicians in determining whether an adolescent student-athlete is presenting with persistent postconcussion symptoms or a typical symptom experience based on their gender and personal health history

Forecasting the specific providers that recipients will perceive as unusually supportive

Perceived support primarily reflects the relationships among specific recipients and providers. These strong relational influences suggest a new approach to interventions: Match specific providers with specific recipients so that unusually supportive relationships emerge. For this approach to be successful, progress must be made on several basic research questions. For example, it must be possible to forecast the specific providers that recipients will perceive as unusually supportive (i.e., forecasting relational support). In 2 studies, support recipients had 3 or 5 conversations with the same providers and reported affect, provider supportiveness, and perceived similarity (Study 2 only) after each conversation. Relational support could be forecasted from recipients' reactions to a single, brief conversation with each provider, even after 4 months had elapsed