Publications by authors named "Prakash Jayakumar"

Background: Measures that quantify clinician fulfillment and engagement with work (clinician experience measures) have the potential to improve the quality, effectiveness, and enjoyment of patient care. Many existing measures of clinician fulfillment and engagement with work are relatively long and potentially burdensome, negatively framed, or address personal well-being. A measure with a small number of items that address positive attributes to which a team aspires may be a more useful measure to guide organizational improvement efforts.

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Background: Variation in levels of pain intensity and incapability among patients with musculoskeletal conditions is associated with measures of mindset (unhelpful thoughts [such as hurt equals harm] and feelings of distress [overwhelm, rumination] regarding bodily sensations) and circumstances (social health as well as security in finances, roles, home, and support) as much or more so than pathophysiology severity. Loneliness is an important aspect of social health, it is associated with worse mental health, and it has been identified as worthy of attention and intervention by health authorities in several countries. It is estimated that up to one-third of adults older than 45 years of age experience loneliness.

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Article Synopsis
  • - This study aimed to create a convolutional neural network (CNN) to detect and classify fractures, focusing on specific characteristics like greater tuberosity displacement and neck-shaft angle, using plain X-rays.
  • - The CNN was trained with over 1,700 X-rays from Australia and validated with data from the Netherlands, comparing results with CT scans evaluated by experts.
  • - The CNN demonstrated a high detection accuracy of 94% for fractures, but less effectiveness in identifying specific fracture characteristics, particularly showing lower performance for greater tuberosity displacement and neck-shaft angles.
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Background: Patient-reported experience measures (PREMs), such as the Jefferson Scale of Patient Perceptions of Physician Empathy (JSPPPE) or the Wake Forest Trust in Physician Scale (WTPS), have notable intercorrelation and ceiling effects (the proportion of observations with the highest possible score). Information is lost when high ceiling effects occur as there almost certainly is at least some variation among the patients with the highest score that the measurement tool was unable to measure. Efforts to identify and quantify factors associated with diminished patient experience can benefit from a PREM with more variability and a smaller proportion of highest possible scores (that is, a more limited ceiling effect) than occurs with currently available PREMs.

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Background: Evidence is mounting that the biopsychosocial paradigm is more accurate and useful than the biomedical paradigm of care. Habits of thought can hinder the implementation of this knowledge into daily care strategies. To understand and lessen these potential barriers, we asked: 1) What is the relative implicit and explicit attitudes of musculoskeletal surgeons towards the biomedical or biopsychosocial paradigms of medicine? 2) What surgeon factors are associated with these attitudes?

Methods: An online survey-based experiment was distributed to members of the Science of Variation Group (SOVG) with a total of 163 respondents.

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Article Synopsis
  • Osteopenia, osteoporosis, and fragility fractures are major public health issues, and the study aimed to identify personal risk factors for these conditions using the UK Biobank data.
  • Researchers analyzed data from over 502,000 participants, focusing on 39 variables to determine their association with osteoporosis, fragility fractures, and osteopenia through statistical methods.
  • Key risk factors found include increased TV watching, living in deprived areas, infrequent social visits, anxiety, depression, and decreased exercise, all of which highlight the importance of lifestyle and environmental influences on bone health.
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Objectives: To determine the relative influence of mindset and fracture severity on 9-month recovery trajectories of pain and capability after upper extremity fractures.

Design: Secondary use of longitudinal data.

Setting: Single Level-1 trauma center in Oxford, United Kingdom.

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When a large language model was prompted to discuss upper extremity conditions and correct instances of misinformation in responses, there was notable persistent misinformation, reinforcement of unhelpful thinking and reduction of independent management of one's health (agency).

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Background: There is mounting evidence that, among musculoskeletal patients, variation in capability has more notable associations with variations in mental and social health factors than with variation in pathophysiology severity. This study sought factors that could limit the integration of this evidence into more comprehensive care models.

Methods: In two scenario-based experiments, surgeon participants in an international collaborative, the Science of Variation Group, reviewed scenarios of (a) nontraumatic (83 participants) and (b) trauma-related (130 participants) pathophysiologies for which tests and treatments were discretionary.

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Background: Primary total knee arthroplasty (TKA) aims to improve the level of capability (ability to perform valued life activities) associated with knee osteoarthritis (OA). However, some evidence suggests a substantial proportion of patients remain dissatisfied with their outcomes after this procedure. We sought to better understand the association between mental health, specifically symptoms of depression, with postoperative outcomes.

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Background: Symptoms of depression have been associated with greater incapability following total hip arthroplasty (THA). A brief, 2-question, measure of symptoms of depression - the Patient Health Questionnaire-2 (PHQ-2) - may be sufficient to measure associations with the magnitude of incapability during recovery from THA. This study investigated whether preoperative symptoms of depression (measured with the PHQ-2) correlated with levels of incapability 6 weeks and 6 months after THA, accounting for demographic and clinical factors.

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Background: People who take active responsibility for their health demonstrate agency. Agency in the context of chronic illness management with disease-modifying treatments is commonly linked to adherence and confidence in care seeking. In musculoskeletal health, agency is commonly observed in the accommodation of conditions related to aging and reflected in studies of people not seeking care.

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Article Synopsis
  • The study explores the use of machine-learning prediction models in orthopaedic trauma, aiming to provide an overview, review reporting practices using the TRIPOD statement, and assess bias with the PROBAST tool.
  • Out of 3,252 studies screened, 45 ML models were identified, with most focusing on hip fractures, and common outcomes being mortality and length of hospital stay.
  • Findings reveal that while many models exist, they often have poor transparency in reporting (62% completeness) and a high risk of bias, primarily due to small sample sizes and inadequate analysis methods.
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Objective: To compare performance between linear regression (LR) and artificial neural network (ANN) models in estimating 9-month patient-reported outcomes (PROs) after upper extremity fractures using various subsets of early mental, social, and physical health variables.

Methods: We studied 734 patients with isolated shoulder, elbow, or wrist fracture who completed demographics, mental and social health measures, and PROs at baseline, 2-4 weeks, and 6-9 months postinjury. PROs included 3 measures of capability (QuickDASH, PROMIS-UE-PF, PROMIS-PI) and one of pain intensity.

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Patient-reported outcome (PRO) measurements are validated tools developed to quantify self-reported aspects of capability, mindset, and circumstances in a standardized fashion. While PRO measurements have primarily been used in the research realm, a growing body of work now underscores substantial opportunities in applying the data generated by these tools to advance patient-centered musculoskeletal care. Specifically, the insights into a patient's health status derived from these measures can augment the standard biomedical approach to the management of patients with orthopaedic trauma.

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Electronic health records are often incomplete, reducing the power of genetic association studies. For some diseases, such as knee osteoarthritis where the routine course of diagnosis involves an X-ray, image-based phenotyping offers an alternate and unbiased way to ascertain disease cases. We investigated this by training a deep-learning model to ascertain knee osteoarthritis cases from knee DXA scans that achieved clinician-level performance.

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Introduction: Low levels of health literacy have been shown to increase healthcare utilization and negatively affect health outcomes within medical specialties. However, the relationship of health literacy with clinical, patient-centered, and process-oriented surgical outcomes is not as well understood.

Materials And Methods: We sought to systematically review the current evidence base regarding the relationship between health literacy and a range of outcomes in patients experiencing surgical care.

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Patient-reported outcome (PRO) measures offer a unique opportunity to systematically incorporate patient perspectives of their health and wellbeing into treatment, view progress over time, and develop opportunities related to clinical decision support, shared decision making, and outcomes-driven payment and practice transformation. As healthcare institutions increasingly look toward adopting PRO measures in their practices, more information is needed regarding the practicalities of implementing a successful PRO program outside of the research setting. This article offers a stepwise approach to implementing a PRO program while overcoming barriers and leveraging facilitators that commonly exist in the realms of human contribution or team building, technological infrastructure, and process design.

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