Applying a perceptions and practicalities approach to understanding nonadherence to antiepileptic drugs

Summary Objective Nonadherence to antiepileptic drugs (AEDs) is a common cause of poor seizure control. This study examines whether reported adherence to AEDs is related to variables identified in the National Institute for Health and Clinical Excellence (NICE) Medicines Adherence Guidelines as being important to adherence: perceptual factors (AED necessity beliefs and concerns), practical factors (limitations in capability and resources), and perceptions of involvement in treatment decisions. Methods This was a cross-sectional study of people with epilepsy receiving AEDs. Participants completed an online survey hosted by the Epilepsy Society (n = 1,010), or as an audit during inpatient admission (n = 118). Validated questionnaires, adapted for epilepsy, assessed reported adherence to AEDs (Medication Adherence Report Scale [MARS]), perceptions of AEDs (Beliefs about Medicines Questionnaire [BMQ]), and patient perceptions of involvement in treatment decisions (Treatment Empowerment Scale [TES]). Results Low adherence was related to AED beliefs (doubts about necessity: t(577) = 3.90, p < 0.001; and concerns: t(995) = 3.45, p = 0.001), reported limitations in capability and resources (t(589) = 7.78, p < 0.001), and to perceptions of a lack of involvement in treatment decisions (t(623) = 4.48, p < 0.001). In multiple logistic regression analyses, these factors significantly (p < 0.001) increased variance in reported adherence, above that which could be explained by age and clinical variables (seizure frequency, type, epilepsy duration, number of AEDs prescribed). Significance Variables identified in the NICE Medicines Adherence Guidelines as potentially important factors for adherence were found to be related to adherence to AEDs. These factors are potentially modifiable. Interventions to support optimal adherence to AEDs should be tailored to address doubts about AED necessity and concerns about harm, and to overcome practical difficulties, while engaging patients in treatment decisions

No blank slates: Pre-existing schemas about pharmaceuticals predict memory for side effects

OBJECTIVES: Attribution of symptoms as medication side effects is informed by pre-existing beliefs about medicines and perceptions of personal sensitivity to their effects (pharmaceutical schemas). We tested whether (1) pharmaceutical schemas were associated with memory (recall/recognition) for side effect information (2) memory explained the attribution of a common unrelated symptom as a side effect. DESIGN: In this analogue study participants saw the patient leaflet of a fictitious asthma drug listing eight side effects. MAIN OUTCOME MEASURES: We measured recall and recognition memory for side effects and used a vignette to test whether participants attributed an unlisted common symptom (headache) as a side effect. RESULTS: Participants who perceived pharmaceuticals as more harmful in general recalled fewer side effects correctly (rCorrect Recall = −.273), were less able to differentiate between listed and unlisted side effects (rRecognition Sensitivity = −.256) and were more likely to attribute the unlisted headache symptom as a side effect (rside effect attribution = .381, ps < .01). The effect of harm beliefs on side effect attribution was partially mediated by correct recall of side effects. CONCLUSION: Pharmaceutical schemas are associated with memory for side effect information. Memory may explain part of the association between pharmaceutical schemas and the attribution of unrelated symptoms as side effects

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging.Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2.Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent