BackgroundFunctional magnetic resonance imaging (fMRI) studies have reported multiple activation foci associated with a variety of conditions, stimuli or tasks. However, most of these studies used fewer than 40 participants.MethodologyAfter extracting data (number of subjects, condition studied, number of foci identified and threshold) from 94 brain fMRI meta-analyses (k = 1,788 unique datasets) published through December of 2011, we analyzed the correlation between individual study sample sizes and number of significant foci reported. We also performed an analysis where we evaluated each meta-analysis to test whether there was a correlation between the sample size of the meta-analysis and the number of foci that it had identified. Correlation coefficients were then combined across all meta-analyses to obtain a summary correlation coefficient with a fixed effects model and we combine correlation coefficients, using a Fisher’s z transformation.Principal FindingsThere was no correlation between sample size and the number of foci reported in single studies (r = 0.0050) but there was a strong correlation between sample size and number of foci in meta-analyses (r = 0.62, p<0.001). Only studies with sample sizes <45 identified larger (>40) numbers of foci and claimed as many discovered foci as studies with sample sizes ≥45, whereas meta-analyses yielded a limited number of foci relative to the yield that would be anticipated from smaller single studies.ConclusionsThese results are consistent with possible reporting biases affecting small fMRI studies and suggest the need to promote standardized large-scale evidence in this field. It may also be that small studies may be analyzed and reported in ways that may generate a larger number of claimed foci or that small fMRI studies with inconclusive, null, or not very promising results may not be published at all.

BACKGROUNDThough current hospital paging systems are neither efficient (callbacks disrupt workflow), nor secure (pagers are not Health Insurance Portability and Accountability Act [HIPAA]-compliant), they are routinely used to communicate patient information. Smartphone-based text messaging is a potentially more convenient and efficient mobile alternative; however, commercial cellular networks are also not secure.OBJECTIVETo determine if augmenting one-way pagers with Medigram, a secure, HIPAA-compliant group messaging (HCGM) application for smartphones, could improve hospital team communication.DESIGNEight-week prospective, cluster-randomized, controlled trialSETTINGStanford HospitalINTERVENTIONThree inpatient medicine teams used the HCGM application in addition to paging, while two inpatient medicine teams used paging only for intra-team communication.MEASUREMENTSBaseline and post-study surveys were collected from 22 control and 41 HCGM team members.RESULTSWhen compared with paging, HCGM was rated significantly (P < 0.05) more effective in: (1) allowing users to communicate thoughts clearly (P = 0.010) and efficiently (P = 0.009) and (2) integrating into workflow during rounds (P = 0.018) and patient discharge (P = 0.012). Overall satisfaction with HCGM was significantly higher (P = 0.003). 85% of HCGM team respondents said they would recommend using an HCGM system on the wards.CONCLUSIONSSmartphone-based, HIPAA-compliant group messaging applications improve provider perception of in-hospital communication, while providing the information security that paging and commercial cellular networks do not. Journal of Hospital Medicine 2014;9:573–578. © 2014 The Authors Journal of Hospital Medicine published by Wiley Periodicals, Inc. on behalf of Society of Hospital Medicine

While genetic testing gains adoption in specialty services such as oncology, neurology, and cardiology, use of genetic and genomic testing has yet to be adopted as widely in primary care. The purpose of this study is to identify and compare patient and primary care provider (PCP) expectations of genetics services in primary care. Patient and PCP perspectives were assessed through a mixed-method approach combining an online survey and semi-structured interviews in a primary care department of a large academic medical institution. A convenience sample of 100 adult primary care patients and 26 PCPs was gathered. The survey and interview questions focused on perceptions of genetic testing, experience with genetic testing, and expectations of genetic services in primary care. Patients felt that their PCP was knowledgeable about genetic testing and expected their PCP to be the first to recognize a need for genetic testing based on family history. Nonetheless, patients reported that PCPs rarely used family history information to discuss genetic risks or order testing. In contrast, PCPs felt uncertain about the clinical utility and scientific value of genetic testing. PCPs were concerned that genetic testing could cause anxiety, frustration, discrimination, and reduced insurability, and that there was unequal access to testing. PCPs described themselves as being "gatekeepers" to genetic testing but did not feel confident or have the desire to become experts in genetic testing. However, PCPs were open to increasing their working knowledge of genetic testing. Within this academic medical center, there is a gap between what patients expect and what primary care providers feel they are adequately prepared to provide in terms of genetic testing services.

ObjectivesAn ‘information gap’ has been identified regarding the effects of chronic disease on occupational injury risk. We investigated the association of ischaemic heart disease, hypertension, diabetes, depression and asthma with acute occupational injury in a cohort of manufacturing workers from 1 January 1997 through 31 December 2007.MethodsWe used administrative data on real-time injury, medical claims, workplace characteristics and demographics to examine this association. We employed a piecewise exponential model within an Andersen–Gill framework with a frailty term at the employee level to account for inclusion of multiple injuries for each employee, random effects at the employee level due to correlation among jobs held by an employee, and experience on the job as a covariate.ResultsOne-third of employees had at least one of the diseases during the study period. After adjusting for potential confounders, presence of these diseases was associated with increased hazard of injury: heart disease (HR 1.23, 95% CI 1.11 to 1.36), diabetes (HR 1.17, 95% CI 1.08 to 1.27), depression (HR 1.25, 95% CI 1.12 to 1.38) and asthma (HR 1.14, 95% CI 1.02 to 1.287). Hypertension was not significantly associated with hazard of injury. Associations of chronic disease with injury risk were less evident for more serious reportable injuries; only depression and a summary health metric derived from claims remained significantly positive in this subset.ConclusionsOur results suggest that chronic heart disease, diabetes and depression confer an increased risk for acute occupational injury.

Numerous functional magnetic resonance imaging (fMRI) studies have reported sex differences. To empirically evaluate for evidence of excessive significance bias in this literature, we searched for published fMRI studies of human brain to evaluate sex differences, regardless of the topic investigated, in Medline and Scopus over 10 years. We analyzed the prevalence of conclusions in favor of sex differences and the correlation between study sample sizes and number of significant foci identified. In the absence of bias, larger studies (better powered) should identify a larger number of significant foci. Across 179 papers, median sample size was n = 32 (interquartile range 23-47.5). A median of 5 foci related to sex differences were reported (interquartile range, 2-9.5). Few articles (n = 2) had titles focused on no differences or on similarities (n = 3) between sexes. Overall, 158 papers (88%) reached “positive” conclusions in their abstract and presented some foci related to sex differences. There was no statistically significant relationship between sample size and the number of foci (−0.048% increase for every 10 participants, p = 0.63). The extremely high prevalence of “positive” results and the lack of the expected relationship between sample size and the number of discovered foci reflect probable reporting bias and excess significance bias in this literature.

BackgroundSouth Korea and surrounding countries in East Asia are believed to have the highest proportion in the world of high frequency hearing loss due to occupational noise exposure, yet there has been limited information published in international journals, and limited information for control of noise in local workplaces beyond strategies from western countries. We exploit medical surveillance information from two worker groups to enhance local knowledge about noise-induced hearing loss and explore the possible importance of shift work to risk.MethodsFour-years of hearing data were evaluated for 81 male farm machine factory workers and 371 male firefighters who had successfully completed a health examination and questionnaires for the duration of the study period. The averages of hearing thresholds at 2, 3, and 4 kHz were used as the primary end-point for comparison. Repeat measure analysis adjusted for age, exposure duration and smoking status was used to measure the difference in hearing threshold between the two groups.ResultsNoise levels were measured in the factory at a mean of 82 dBA, with a range of 66-97. No concurrent measurements were taken for the firefighters, but historic comparison values showed a wider range but a similar mean of 76-79 dBA. Although losses during follow-up were negligible, the factory workers had significantly (P < 0.0001) more hearing loss at the baseline of the study than the firefighters in both ears at 2, 3, and 4 kHz, adjusted for age, duration of employment and smoking status. Among those with 10 years of employment, mean losses at these frequencies among the factory workers fell into the impairment range (> 25 dB loss). Firefighters also showed increased losses associated with longer exposure duration, but these were significantly less marked. Losses at lower frequencies (< or = 1 kHz) were negligible in both groups.ConclusionsKorean work environments with continuous noise exposure in the measured range should consider implementation of a hearing conservation program. Further evaluation of hearing loss in workers exposed to irregular or intermittent high noise levels, such as firefighters, is also warranted.

BACKGROUND: This study sought to develop a predictive model for 30-day mortality in hospitalized cancer patients, by using admission information available through the electronic medical record. METHODS: Observational cohort study of 3062 patients admitted to the oncology service from August 1, 2008, to July 31, 2009. Matched numbers of patients were in the derivation and validation cohorts (1531 patients). Data were obtained on day 1 of admission and included demographic information, vital signs, and laboratory data. Survival data were obtained from the Social Security Death Index. RESULTS: The 30-day mortality rate of the derivation and validation samples were 9.5% and 9.7% respectively. Significant predictive variables in the multivariate analysis included age (P <.0001), assistance with activities of daily living (ADLs; P ¼.022), admission type (elective/emergency) (P ¼.059), oxygen use (P <.0001), and vital signs abnormalities including pulse oximetry (P ¼.0004), temperature (P ¼.017), and heart rate (P ¼.0002). A logistic regression model was developed to predict death within 30 days: Score ¼ 18.2897 þ 0.6013*(admit type) þ 0.4518*(ADL) þ 0.0325*(admit age) À 0.1458*(temperature) þ 0.019*(heart rate) À 0.0983*(pulse oximetry) À 0.0123 (systolic blood pressure) þ 0.8615*(O 2 use). The largest sum of sensitivity (63%) and specificity (78%) was at À2.09 (area under the curve ¼ À0.789). A total of 25.32% (100 of 395) of patients with a score above À2.09 died, whereas 4.31% (49 of 1136) of patients below À2.09 died. Sensitivity and positive predictive value in the derivation and validation samples compared favorably. CONCLUSIONS: Clinical factors available via the electronic medical record within 24 hours of hospital admission can be used to identify cancer patients at risk for 30-day mortality. These patients would benefit from discussion of preferences for care at the end of life.

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