SUMMARY[18F]-Fluoro-D-deoxyglucose positron emission tomography (FDG-PET) is used in the complex presurgical evaluation of patients with medically intractable temporal lobe epilepsy (TLE) (Debets et al

Patients who were considered to be well-controlled proved to report an unexpectedly high number of subjective complaints. Both medication and aspects of personality contributed to the level of complaints. Our study illustrates that subjective side-effects are easily overlooked in everyday clinical practice, possibly because in practice a generally phrased question is used to detect side-effects.

A B S T R A C TThis study is focused on how peer feedback in SPOCs (Small Private Online Courses) can effectively lead to deep learning. Promoting deep learning in online courses, such as SPOCs, is often a challenge. We aimed for deep learning by reinforcement of 'feedback dialogue' as scalable intervention.Students provided peer feedback as a dialogue, both individually and as a group. They were instructed to rate each other's feedback, which was aimed at deep learning. Data from questionnaires from 41 students of a master epidemiology course were used to measure for each feedback assignment to what extent deep learning was perceived. The feedback received by students who scored extremely high or low on the questionnaire was analyzed in order to find out which features of the feedback led to deep learning. In addition, students were interviewed to retrieve information about the underlying mechanisms.The results support the view that peer feedback instruction and peer feedback rating lead to peer feedback dialogues that, in turn, promote deep learning in SPOCs. The value of peer feedback appears to predominantly result from the dialogue it triggers, rather than the feedback itself. Especially helpful for students is the constant attention to how one provides peer feedback: by instruction, by having to rate feedback and therefore by repeatedly having to reflect. The dialogue is strengthened because students question feedback from peers in contrast to feedback from their instructor. As a result, they continue to think longer and deeper, which enables deep learning.

Study design: An experimental cross-sectional design. Objectives: To evaluate whether training of the innervated respiratory muscles in individuals with a (partial) cervical spinal cord injury will improve the strength and endurance capacity of these muscles and the exercise performance in these individuals. Setting: Department of Physiology and Pulmonary diseases, Nijmegen, The Netherlands. Method: In this study nine individuals with tetraplegia (C3 ± C7) performed a target¯ow endurance training of the inspiratory muscles, twice a day for 15 min. First, the subjects performed a`sham' training for 6 weeks with no appreciable resistance, after that they performed a`real' training for 6 weeks with a resistance of 70% of the maximal endurance capacity of the inspiratory muscles. The training was evaluated at 0, 6 and 12 weeks by the following tests: (1) the slow Inspiratory Vital Capacity (IVC) and the Forced Inspiratory and Expiratory Volumes over 1 s (FIV 1 and FEV 1 ); (2) the Maximal Inspiratory Mouth Pressure (P imax ) and the Endurance Pressure (P endu ) and (3) a maximal arm-cranking exercise test. Results: After the sham training, the P endu was increased from 3.98 to 4.71 kPa with a Pvalue of 0.05. The sham training had no in¯uence on any of the other variables. The real training had no eect on the IVC, FIV 1 , FEV 1 and P imax , however, increased the P endu from 4.71 to 6.16 kPa (P=0.01), representing the respiratory muscle-endurance capacity. The oxygen consumption (V . O 2 peak) in the maximal exercise test improved from 0.87 to 0.98 l/ min (P=0.05). Conclusion:The results of the study indicate that training of the respiratory muscles results in an enhanced endurance capacity of these muscles and a concomitant increase in the aerobic exercise performance.

Introduction Social interaction is key in educational success. In online education, the creation of social interaction may be a challenge. This observational study evaluated to what extent social interactions occur during small private online courses (SPOCs). Methods Discussion forums of four courses of the UMC Utrecht's international Master's Program Epidemiology were assessed and posts were categorized as either content specific, functional/technical, or social. Results SPOCs at University Medical Center Utrecht showed substantial social interaction, creating involvement and student coherence, combined with students discussing and explaining content to each other. Interactions play a major role in SPOCs. Our results show that 43% of all discussion posts were social; 90% of social posts were initiated by students; and 94% was aimed at students. Conclusion SPOCs appear to provide a sustainable answer to the increased demand for online higher education, with an environment suitable for students to learn, in agreement with the need for social interaction in higher education.

SUMMARYPurpose: Although several independent predictors of seizure freedom after temporal lobe epilepsy surgery have been identified, their combined predictive value is largely unknown. Using a large database of operated patients, we assessed the combined predictive value of previously reported predictors included in a single multivariable model. were not important predictors of seizure freedom. Among patients with a high probability of seizure freedom, 85% were seizure-free one year after surgery; however, among patients with a high risk of not becoming seizure-free, still 40% were seizure-free one year after surgery. Conclusion: We could only moderately predict seizure freedom after temporal lobe epilepsy surgery. It is particularly difficult to predict who will not become seizure-free after surgery.

We investigated the relation between providing and receiving audio peer feedback with a deep approach to learning within online education. Online students were asked to complete peer feedback assignments. Data through a questionnaire with 108 respondents and 14 interviews were used to measure to what extent deep learning was perceived and why. Results support the view that both providing and receiving audio peer feedback indeed promote deep learning. As a consequence of the peer feedback method, the following student mechanisms were triggered: “feeling personally committed,” “probing back and forth,” and “understanding one's own learning process.” Particularly important for both providing and receiving feedback is feeling personally committed. Results also show that mechanisms were a stronger predictor for deep learning when providing than when receiving. Given the context in which instructors face an increasing number of students and a high workload, students may be supported by online audio peer feedback as a method to choose a deep approach to learning.

Higher education aims for deep learning and increasingly uses a specific form of online education: Small Private Online Courses (SPOCs). To overcome challenges that instructors face in order to promote deep learning through that format, the use of feedback may have significant potential. We interviewed eleven instructors and four students and organized a focus group to formulate scalable design propositions for instructors in SPOCs to promote deep learning. Propositions have been formulated according to the CIMO-logic. This study resulted in identification of four mechanisms by which the desired outcome (deep learning) can be achieved, which we describe here along with proposed interventions. Results show that the "online learning interaction model" can be deepened with these mechanisms: 1) Feeling personally committed, 2) Asking and providing relevant feedback, 3) Probing back and forth, and 4) Understanding one's own learning process. To activate these mechanisms, scalable feedback interventions are described in three categories. Results at this relatively young field of SPOCs also show that feedback as a dialogical process may contribute to solving the current challenges of instructors in SPOCs to achieve deep learning with their students.