Articles: pain-measurement.
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Comparative Study
A Comparative Analysis of Pain Assessment Methods in the Initial Postoperative Phase Following Different Pilonidal Cyst Surgeries.
Background and Objectives: In this study, we aimed to evaluate pain intensity in patients after pilonidal disease surgeries of varying extent using pressure algometry and the visual analog scale and to explore potential correlations between these methods. Materials and Methods: A total of 78 adult patients with symptomatic pilonidal cysts were enrolled in this study. The patients were divided into two groups based on the type of surgery assigned to each patient at the pre-hospital consultation: pit-picking surgery (n = 39) and radical excision (n = 39). ⋯ Conclusions: In the early postoperative period following pilonidal disease surgery of varying extents, pain measured with the VAS does not differ. In contrast, the pressure algometry method showed greater pain in the minimally invasive surgery cohort on the first postoperative day. However, further larger studies are needed to compare these pain assessment methods in reporting pain intensity experienced during patient movement.
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Gabapentinoids are commonly prescribed to control neuropathic pain of lumbar radiculopathy. Few trials have compared the efficacy of gabapentin (GBP) and pregabalin (PGB). Therefore, the authors conducted a meta-analysis to compare the difference in effect between GBP and PGB in lumbar radiculopathy patients. ⋯ Based on this article, the existing evidence suggests that PGB was more effective in reducing pain of lumbar radiculopathy compared to GBP at the short-term follow-up, but there was no difference in the long-term follow-up. Physicians should consider this finding in prescribing medications for patients with lumbar radiculopathy.
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Observational Study
An investigation of machine learning algorithms for prediction of temporomandibular disorders by using clinical parameters.
This study aimed to predict temporomandibular disorder (TMD) using machine learning (ML) approaches based on measurement parameters that are practically acquired in clinical settings. 125 patients with TMD and 103 individuals without TMD were included in the study. Pain intensity (with visual analog scale), maximum mouth opening (MMO) and lateral excursion movements (with millimeter ruler), cervical range of motion (with goniometer), pressure pain threshold (PPT; with algometer), oral parafunctional behaviors (with Oral Behaviors Checklist), psychological status (with Hospital Anxiety and Depression Scale), and quality of life (with Oral Health Impact Profile) were evaluated. The measurements were analyzed via over 20 ML algorithms, taking into account an extensive parameter tuning and cross-validation process. ⋯ According to this model, the 5 most important variables for predicting TMD were pain intensity, MMO, lateral excursion and PPT values of masseter and temporalis anterior muscles, respectively. The Bagging algorithm using the MARS algorithm is a robust model that, in combination with clinical parameters, assists in the detection of patients with TMD in settings with limited capabilities. The clinical parameters and ML algorithm proposed in this study may assist clinicians inexperienced in TMD to make a preliminary detection of TMD in clinics where diagnostic imaging tools are limited.
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The variability in pain drawing styles and analysis methods has raised concerns about the reliability of pain drawings as a screening tool for nonpain symptoms. In this study, a data-driven approach to pain drawing analysis has been used to enhance the reliability. The aim was to identify distinct clusters of pain patterns by using latent class analysis (LCA) on 46 predefined anatomical areas of a freehand digital pain drawing. ⋯ Statistically significant differences were found between these clusters in every self-reported health domain. Similarly, for both LBP and MBPNP, pain drawings involving more extensive pain areas were associated with higher activity limitation, more intense pain, and more psychological distress. This study presents a versatile data-driven approach for analyzing pain drawings to assist in managing spinal pain.