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Objective: For people who experience social inequities and structural violence, pain and related care are inexorably linked to experiences of injustice and stigma. The purpose of this study was to examine in greater depth the experiences of pain and discrimination and stigma across diverse marginalized communities in order to recommend equity-oriented healthcare approaches., Methods: This community-based qualitative study reports on four focus groups that included 36 people living with pain. All participants identified with one of three groups known to experience high levels of inequities and structural violence including an Indigenous group, a LGBTQ2S group, and two newcomer and refugee groups., Results: Pain was entangled with and shaped by: social locations and identities, experiences of violence, trauma and related mental health issues, experiences of discrimination, stigma and dismissal, experiences of inadequate and ineffective health care, and the impacts of these intersecting experiences., Conclusions: Equity-oriented responses to chronic pain would recognize pain not only as a biomedical issue but as a social justice issue. The EQUIP Framework is an approach to integrating trauma- and violence-informed care; culturally-safe care; and harm reduction in health care that may hold promise for being tailored to people experiencing pain and social marginalization.

Analgesic trials pose unique scientific, ethical, and practical challenges in pediatrics. Participants in a scientific workshop sponsored by the US Food and Drug Administration developed consensus on aspects of pediatric analgesic clinical trial design. The standard parallel-placebo analgesic trial design commonly used for adults has ethical and practical difficulties in pediatrics, due to the likelihood of subjects experiencing pain for extended periods of time. Immediate-rescue designs using opioid-sparing, rather than pain scores, as a primary outcome measure have been successfully used in pediatric analgesic efficacy trials. These designs maintain some of the scientific benefits of blinding, with some ethical and practical advantages over traditional designs. Preferred outcome measures were recommended for each age group. Acute pain trials are feasible for children undergoing surgery. Pharmacodynamic responses to opioids, local anesthetics, acetaminophen, and nonsteroidal antiinflammatory drugs appear substantially mature by age 2 years. There is currently no clear evidence for analgesic efficacy of acetaminophen or nonsteroidal antiinflammatory drugs in neonates or infants younger than 3 months of age. Small sample designs, including cross-over trials and N of 1 trials, for particular pediatric chronic pain conditions and for studies of pain and irritability in pediatric palliative care should be considered. Pediatric analgesic trials can be improved by using innovative study designs and outcome measures specific for children. Multicenter consortia will help to facilitate adequately powered pediatric analgesic trials.

The current study examined youths' and their parents' perceptions concerning participation in an investigation of spontaneous and induced pain during recovery from laparoscopic appendectomy. Youth (age range 5-17 years) and their parents independently completed surveys about their study participation. On a 0 (very negative) -to-10 (very positive) scale, both parents 9.4(1.3) [mean(SD)] and youth 7.9(2.4) rated their experience as positive. Among youth, experience ratings did not differ by pain severity and survey responses did not differ by age. Most youth (83%) reported they would tell another youth to participate. Ethical issues regarding instigation of pain in youth for research purposes are examined.

Objective pain assessment is required for appropriate pain management in the clinical setting. However, clinical gold standard pain assessment is based on subjective methods. Automated pain detection from physiological data may provide important objective information to better standardize pain assessment. Specifically, electrodermal activity (EDA) can identify features of stress and anxiety induced by varying pain levels. However, notable variability in EDA measurement exists and research to date has demonstrated sensitivity but lack of specificity in pain assessment. In this paper, we use timescale decomposition (TSD) to extract salient features from EDA signals to identify an accurate and automated EDA pain detection algorithm to sensitively and specifically distinguish pain from no-pain conditions.

Accurately determining pain levels in children is difficult, even for trained professionals and parents. Facial activity provides sensitive and specific information about pain, and computer vision algorithms have been developed to automatically detect Facial Action Units (AUs) defined by the Facial Action Coding System (FACS). Our prior work utilized information from computer vision, i.e., automatically detected facial AUs, to develop classifiers to distinguish between pain and no-pain conditions. However, application of pain/no-pain classifiers based on automated AU codings across different environmental domains results in diminished performance. In contrast, classifiers based on manually coded AUs demonstrate reduced environmentally-based variability in performance. In this paper, we train a machine learning model to recognize pain using AUs coded by a computer vision system embedded in a software package called iMotions. We also study the relationship between iMotions (automatically) and human (manually) coded AUs. We find that AUs coded automatically are different from those coded by a human trained in the FACS system, and that the human coder is less sensitive to environmental changes. To improve classification performance in the current work, we applied transfer learning by training another machine learning model to map automated AU codings to a subspace of manual AU codings to enable more robust pain recognition performance when only automatically coded AUs are available for the test data. With this transfer learning method, we improved the Area Under the ROC Curve (AUC) on independent data from new participants in our target domain from 0.67 to 0.72.

Clinicians tend to assign greater weight to non-verbal expression than to patients'' self-report when judging the location and severity of pain. Judgments can misrepresent the actual experience because patients can successfully alter their pain expressions. The present research provides a basis for discriminating genuine and deceptive pain expressions by expanding detailed accounts of facial expressions to include previously unexamined variables, including study of temporal patterns and contiguity of facial actions as well as the occurrence of specific deception cues. Low back patients'' facial expressions (n=40) were videotaped at rest and while undergoing a painful straight leg raise with instructions to: (1) genuinely express their pain, or (2) pretend that it did not hurt. As well, they were asked to fake pain without moving. The Facial Action Coding System was used to describe and quantify facial activity. The different types of expression were compared on the frequency, type, intensity, temporal pattern and contiguity of facial actions, as well as on the frequency of specific deception cues. Findings confirmed the difficulty of discriminating the facial expressions, but indicated that faked pain expressions show a greater number of pain-related and non-pain-related actions, have a longer peak intensity and overall duration, and the facial actions observed tend to be less temporally contiguous than are those in genuine pain expressions. The differences between masked pain and neutral expressions were subtle, with a greater frequency of mouth opening and residual eyebrow movement in masked pain expressions. Thus, there is an empirical basis for discriminating genuine and deceptive facial displays. [Copyright &y& Elsevier]