Towards an optimal therapy strategy for myogenous TMD, physiotherapy compared with occlusal splint therapy in an RCT with therapy-and-patient-specific treatment durations

Appendix. Threshold of signs and symptoms. Post-hoc power analysis on measures of effectiveness. Two-way ANOVA statistical analysis for pain intensity. Rules for progressing and ending splint therapy. Rules for progressing and ending physiotherapy. A stepped-care model including two possible therapies. (PDF 86 kb

TOI-836 : a super-Earth and mini-Neptune transiting a nearby K-dwarf

Funding: TGW, ACC, and KH acknowledge support from STFC consolidated grant numbers ST/R000824/1 and ST/V000861/1, and UKSA grant ST/R003203/1.We present the discovery of two exoplanets transiting TOI-836 (TIC 440887364) using data from TESS Sector 11 and Sector 38. TOI-836 is a bright (T = 8.5 mag), high proper motion (∼200 mas yr−1), low metallicity ([Fe/H]≈−0.28) K-dwarf with a mass of 0.68 ± 0.05 M⊙ and a radius of 0.67 ± 0.01 R⊙. We obtain photometric follow-up observations with a variety of facilities, and we use these data-sets to determine that the inner planet, TOI-836 b, is a 1.70 ± 0.07 R⊕ super-Earth in a 3.82 day orbit, placing it directly within the so-called ‘radius valley’. The outer planet, TOI-836 c, is a 2.59 ± 0.09 R⊕ mini-Neptune in an 8.60 day orbit. Radial velocity measurements reveal that TOI-836 b has a mass of 4.5 ± 0.9 M⊕, while TOI-836 c has a mass of 9.6 ± 2.6 M⊕. Photometric observations show Transit Timing Variations (TTVs) on the order of 20 minutes for TOI-836 c, although there are no detectable TTVs for TOI-836 b. The TTVs of planet TOI-836 c may be caused by an undetected exterior planet.Publisher PDFPeer reviewe

TOI-836: A super-Earth and mini-Neptune transiting a nearby K-dwarf

We present the discovery of two exoplanets transiting TOI-836 (TIC 440887364) using data from TESS Sector 11 and Sector 38. TOI-836 is a bright ($T = 8.5$ mag), high proper motion ($\sim\,200$ mas yr$^{-1}$), low metallicity ([Fe/H]$\approx\,-0.28$) K-dwarf with a mass of $0.68\pm0.05$ M$_{\odot}$ and a radius of $0.67\pm0.01$ R$_{\odot}$. We obtain photometric follow-up observations with a variety of facilities, and we use these data-sets to determine that the inner planet, TOI-836 b, is a $1.70\pm0.07$ R$_{\oplus}$ super-Earth in a 3.82 day orbit, placing it directly within the so-called 'radius valley'. The outer planet, TOI-836 c, is a $2.59\pm0.09$ R$_{\oplus}$ mini-Neptune in an 8.60 day orbit. Radial velocity measurements reveal that TOI-836 b has a mass of $4.5\pm0.9$ M$_{\oplus}$ , while TOI-836 c has a mass of $9.6\pm2.6$ M$_{\oplus}$. Photometric observations show Transit Timing Variations (TTVs) on the order of 20 minutes for TOI-836 c, although there are no detectable TTVs for TOI-836 b. The TTVs of planet TOI-836 c may be caused by an undetected exterior planet

The index 'Treatment Duration Control' for enabling randomized controlled trials with variation in duration of treatment of chronic pain patients

BACKGROUND: Treatment duration varies with the type of therapy and a patient’s recovery speed. Including such a variation in randomized controlled trials (RCTs) enables comparison of the actual therapeutic potential of different therapies in clinical care. An index, Treatment Duration Control (TDC) of outcome scores was developed to help decide when to end treatment and also to determine treatment outcome by a blinded assessor. In contrast to traditional Routine Outcome Monitoring which considers raw score changes, TDC uses relative change. METHODS: Our theory shows that if a patient with the largest baseline scores in a sample requires a relative decrease by treatment factor T to reach a zone of low score values (functional status), any patient with smaller baselines will attain functional status with T. Furthermore, the end score values are proportional to the baseline. These characteristics concur with findings from the literature that a patient’s assessment of ‘much improved’ following treatment (related to attaining functional status) is associated with a particular relative decrease in pain intensity yielding a final pain intensity that is proportional to the baseline. Regarding the TDC-procedure: those patient’s scores that were related to pronounced signs and symptoms, were selected for adaptive testing (reference scores). A Contrast-value was determined for each reference score between its reference level and a subsequent level, and averaging all Contrast-values yielded TDC. A cut-off point related to factor T for attaining functional status, was the TDC-criterion to end a patient’s treatment as being successful. The use of TDC has been illustrated in RCT data from 118 chronic pain patients with myogenous Temporomandibular Disorders, and the TDC-criterion was validated. RESULTS: The TDC-criterion of successful/unsuccessful treatment approximated the cut-off separating two patient subgroups in a bimodal post-treatment distribution of TDC-values. Pain intensity decreased to residual levels and Health-Related Quality of Life (HRQoL) increased to normal levels, following successful treatment according to TDC. The post-treatment TDC-values were independent from the baseline values of pain intensity or HRQoL, and thus independent from the patient’s baseline severity of myogenous Temporomandibular Disorders. CONCLUSIONS: TDC enables RCTs that have a variable therapy- and patient-specific duration

Horizontal and vertical components of the perisaccadic errors as function of T2 duration.

<p>Data pooled for both monkeys. There is no trend in the data.</p

Gaze-localization accuracy and precision in static and dynamic double steps.

<p>Data are shown for the three different trial types (single, static, dynamic) for T1 (top row) and T2 (bottom). Localization errors are converted into under- and overshoots with respect to the spatial target location. The center of the panels (x = y = 0, circle and intersection of dotted lines) coincides with the target location. Errors of monkey O: filled dots, monkey M: open squares. Error distributions are presented as histograms (bin size one deg, with frequency axis) at the baseline of each axis. Solid distributions: M. Dashed histograms: monkey O. The solid (M) and dashed (O) lines indicate the mean errors.</p

Overall localization performance of both monkeys.

<p>Means and standard deviations (in deg) of gaze-saccade endpoint distritbutions of azimuth (top row) and elevation (bottom row) responses with respect to the target location (origin of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047606#pone-0047606-g006" target="_blank">Fig. 6</a>) for the first (left) and second (right) target flashes. A positive (negative) mean indicates a target overshoot (undershoot) in that component. Gaze shifts tended to slightly undershoot the target. Comparisons for a statistical difference between distributions were made between the same target components and for the same animal, based on a KS test. Static and dynamic double-steps had significantly more endpoint variability than single-step responses (p<0.05) in the majority of cases. The same holds for dynamic vs. static double steps. Endpoint scatter of monkey O saccades was larger than for monkey M (p<0.001).</p

Retinal reconstruction of gaze shifts during T2 presentation.

<p>A) Distribution of gaze-movement amplitudes during T2 presentation in static and dynamic trials for both monkeys. Monkey M solid line with open squares, monkey O dashed line with filled circles. Note differences in scales. B) Reconstructed image of T2 locations on the retina. Reconstruction is based on TE = TS – G, with TE the retinal location of the target, TS its spatial location and G the gaze position. The origin of the plot at [0,0] coincides with the fovea. Top row panels: Monkey M, Bottom row panels: Monkey O, Left panels: Static trials, Right panels: Dynamic trials. In dynamic trials the brief stimulus could produce a considerable streak across the retina.</p

Result of Multiple Linear Regression.

<p>The MLR was applied to the updating models of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047606#pone.0047606.e004" target="_blank">Eq. 4a</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047606#pone.0047606.e006" target="_blank">c</a> and the no-compensation model. Because first-saccade responses were mainly in the horizontal direction, the elevation components lacked sufficient variation. Accordingly, only responses in azimuth are analyzed. Errorbars indicate 95% confidence intervals. The dynamic feedback model yields coefficients that are closest to the ideal values of with α = 1, and β = −1, respectively. The DFB model also gives the highest coefficient of determination (r²), and therefore explains the data best.</p