SMS was delivered with a Medtronic R30 repetitive magnetic stimulator (Medtronic Corporation, Skovlunde, Denmark) connected to a C-B60 figure of eight coil capable of delivering a maximum output of 2 Tesla per pulse. The coil measured 90 mm in each wing and was

centered over the surface landmark corresponding to the cauda equina region. The coil was placed flat over the back with the handle pointing cranially. Each patient on active treatment received 200 trains of five pulses delivered at 10 Hz, at an interval of 5 seconds between each train. “”Sham”" SMS was delivered with the coil angled vertically and one of the wing edges in contact with the stimulation point.

Results. All patients tolerated the procedure well and no side effects of SMS were reported.

In the treatment arm, SMS had resulted in significant pain reduction immediately and at Day

4 after treatment (P < 0.05). selleck compound In the placebo arm, however, no significant pain reduction was seen immediately and at Day 4 after SMS.

SMS in the treatment arm had resulted in mean pain reduction of 62.3% postprocedure and 17.4% at Day 4. The placebo arm only achieved pain reduction of 6.1% postprocedure and 4.5% at Day 4.

Discussion. This is the first study to show that a single session of SMS resulted in significant improvement of pain associated with lumbar spondylosis in a randomized, double-blind, placebo-controlled setting. The novel findings support the potential of this technique for future studies pertaining to neuropathic pain.”
“Background: In network meta-analyses, several treatments can be compared by connecting evidence from find more clinical trials that have investigated two or more treatments. The resulting trial network allows estimating the relative effects of all pairs of treatments taking indirect evidence into

account. For Bucladesine a valid analysis of the network, consistent information from different pathways is assumed. Consistency can be checked by contrasting effect estimates from direct comparisons with the evidence of the remaining network. Unfortunately, one deviating direct comparison may have side effects on the network estimates of others, thus producing hot spots of inconsistency.

Methods: We provide a tool, the net heat plot, to render transparent which direct comparisons drive each network estimate and to display hot spots of inconsistency: this permits singling out which of the suspicious direct comparisons are sufficient to explain the presence of inconsistency. We base our methods on fixed-effects models. For disclosure of potential drivers, the plot comprises the contribution of each direct estimate to network estimates resulting from regression diagnostics. In combination, we show heat colors corresponding to the change in agreement between direct and indirect estimate when relaxing the assumption of consistency for one direct comparison.