ABSTRACT
OBJECTIVE: We explored the comparative effectiveness of available therapies for chronic pain associated with temporomandibular disorders (TMD). DESIGN: Systematic review and network meta-analysis of randomised clinical trials (RCTs). DATA SOURCES: MEDLINE, EMBASE, CINAHL, CENTRAL, and SCOPUS were searched to May 2021, and again in January 2023. STUDY SELECTION: Interventional RCTs that enrolled patients presenting with chronic pain associated with TMD. DATA EXTRACTION AND SYNTHESIS: Pairs of reviewers independently identified eligible studies, extracted data, and assessed risk of bias. We captured all reported patient-important outcomes, including pain relief, physical functioning, emotional functioning, role functioning, social functioning, sleep quality, and adverse events. We conducted frequentist network meta-analyses to summarise the evidence and used the GRADE approach to rate the certainty of evidence and categorise interventions from most to least beneficial. RESULTS: 233 trials proved eligible for review, of which 153-enrolling 8713 participants and exploring 59 interventions or combinations of interventions-were included in network meta-analyses. All subsequent effects refer to comparisons with placebo or sham procedures. Effects on pain for eight interventions were supported by high to moderate certainty evidence. The three therapies probably most effective for pain relief were cognitive behavioural therapy (CBT) augmented with biofeedback or relaxation therapy (risk difference (RD) for achieving the minimally important difference (MID) in pain relief of 1 cm on a 10 cm visual analogue scale: 36% (95% CI 33 to 39)), therapist-assisted jaw mobilisation (RD 36% (95% CI 31 to 40)), and manual trigger point therapy (RD 32% (29 to 34)). Five interventions were less effective, yet more effective than placebo, showing RDs ranging between 23% and 30%: CBT, supervised postural exercise, supervised jaw exercise and stretching, supervised jaw exercise and stretching with manual trigger point therapy, and usual care (such as home exercises, self stretching, reassurance).Moderate certainty evidence showed four interventions probably improved physical functioning: supervised jaw exercise and stretching (RD for achieving the MID of 5 points on the short form-36 physical component summary score: 43% (95% CI 33 to 51)), manipulation (RD 43% (25 to 56)), acupuncture (RD 42% (33 to 50)), and supervised jaw exercise and mobilisation (RD 36% (19 to 51)). The evidence for pain relief or physical functioning among other interventions, and all evidence for adverse events, was low or very low certainty. CONCLUSION: When restricted to moderate or high certainty evidence, interventions that promote coping and encourage movement and activity were found to be most effective for reducing chronic TMD pain. REGISTRATION: PROSPERO (CRD42021258567).
Subject(s)
Chronic Pain , Cognitive Behavioral Therapy , Humans , Chronic Pain/etiology , Chronic Pain/therapy , Network Meta-Analysis , Exercise Therapy/methods , Physical Therapy Modalities , Randomized Controlled Trials as TopicABSTRACT
Spatio-temporal models can be used to analyze data collected at various spatial locations throughout multiple time points. However, even with a finite number of spatial locations, there may be a lack of resources to collect data from every spatial location at every time point. We develop a spatio-temporal finite-population block kriging (ST-FPBK) method to predict a quantity of interest, such as a mean or total, across a finite number of spatial locations. This ST-FPBK predictor incorporates an appropriate variance reduction for sampling from a finite population. Through an application to moose surveys in the east-central region of Alaska, we show that the predictor has a substantially smaller standard error compared to a predictor from the purely spatial model that is currently used to analyze moose surveys in the region. We also show how the model can be used to forecast a prediction for abundance in a time point for which spatial locations have not yet been surveyed. A separate simulation study shows that the spatio-temporal predictor is unbiased and that prediction intervals from the ST-FPBK predictor attain appropriate coverage. For ecological monitoring surveys completed with some regularity through time, use of ST-FPBK could improve precision. We also give an R package that ecologists and resource managers could use to incorporate data from past surveys in predicting a quantity from a current survey.
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INTRODUCTION: Cluster randomized crossover trials are often faced with a dilemma when selecting an optimal model of consent, as the traditional model of obtaining informed consent from participant's before initiating any trial related activities may not be suitable. We describe our experience of engaging patient advisors to identify an optimal model of consent for the PREP-IT trials. This paper also examines surrogate measures of success for the selected model of consent. METHODS: The PREP-IT program consists of two multi-center cluster randomized crossover trials that engaged patient advisors to determine an optimal model of consent. Patient advisors and stakeholders met regularly and reached consensus on decisions related to the trial design including the model for consent. Patient advisors provided valuable insight on how key decisions on trial design and conduct would be received by participants and the impact these decisions will have. RESULTS: Patient advisors, together with stakeholders, reviewed the pros and cons and the requirements for the traditional model of consent, deferred consent, and waiver of consent. Collectively, they agreed upon a deferred consent model, in which patients may be approached for consent after their fracture surgery and prior to data collection. The consent rate in PREP-IT is 80.7%, and 0.67% of participants have withdrawn consent for participation. DISCUSSION: Involvement of patient advisors in the development of an optimal model of consent has been successful. Engagement of patient advisors is recommended for other large trials where the traditional model of consent may not be optimal.
ABSTRACT
Dall's sheep (Ovis dalli dalli) are endemic to alpine areas of sub-Arctic and Arctic northwest America and are an ungulate species of high economic and cultural importance. Populations have historically experienced large fluctuations in size, and studies have linked population declines to decreased productivity as a consequence of late-spring snow cover. However, it is not known how the seasonality of snow accumulation and characteristics such as depth and density may affect Dall's sheep productivity. We examined relationships between snow and climate conditions and summer lamb production in Wrangell-St Elias National Park and Preserve, Alaska over a 37-year study period. To produce covariates pertaining to the quality of the snowpack, a spatially-explicit snow evolution model was forced with meteorological data from a gridded climate re-analysis from 1980 to 2017 and calibrated with ground-based snow surveys and validated by snow depth data from remote cameras. The best calibrated model produced an RMSE of 0.08 m (bias 0.06 m) for snow depth compared to the remote camera data. Observed lamb-to-ewe ratios from 19 summers of survey data were regressed against seasonally aggregated modelled snow and climate properties from the preceding snow season. We found that a multiple regression model of fall snow depth and fall air temperature explained 41% of the variance in lamb-to-ewe ratios (R2 = .41, F(2,38) = 14.89, p<0.001), with decreased lamb production following deep snow conditions and colder fall temperatures. Our results suggest the early establishment and persistence of challenging snow conditions is more important than snow conditions immediately prior to and during lambing. These findings may help wildlife managers to better anticipate Dall's sheep recruitment dynamics.
