Current medical research and opinion
-
Acute pain is among the most common reasons that people consult primary care physicians, who must weigh benefits versus risks of analgesics use for each patient. Paracetamol (acetaminophen) is a first-choice analgesic for many adults with mild to moderate acute pain, is generally well tolerated at recommended doses (≤4 g/day) in healthy adults and may be preferable to non-steroidal anti-inflammatory drugs that are associated with undesirable gastrointestinal, renal, and cardiovascular effects. Although paracetamol is widely used, many patients and physicians still have questions about its suitability and dosing, especially for older people or adults with underlying comorbidities, for whom there are limited clinical data or evidence-based guidelines. ⋯ Paracetamol is a suitable first-line analgesic for mild to moderate acute pain in many adults with liver, kidney or cardiovascular disease, gastrointestinal disorders, asthma, and/or who are older. No evidence supports routine dose reduction for older people. Rather, dosing for adults who are older and/or have decompensated cirrhosis, advanced kidney failure, or analgesic-induced asthma that is known to be cross-sensitive to paracetamol, should be individualized in consultation with their physician, who may recommend a lower effective dose appropriate to the circumstances.
-
To examine the healthcare utilization and costs associated with colorectal cancer (CRC) screening by colonoscopy, including costs associated with post-endoscopy events, among average-risk adults covered by Medicaid insurance. ⋯ This large, claims-based cohort study reports average (SD) out-of-pocket costs for Medicaid beneficiaries at $6 ($132), which could be one factor contributing to the accessibility of CRC screening by colonoscopy. The incidence of events potentially associated with colonoscopy (i.e. within 30 days after the screening) was 3-4%, and the event-related costs were considerable.
-
Review Meta Analysis
Comorbidities and autopsy findings of COVID-19 deaths and their association with time to death: a systematic review and meta-analysis.
Examination of postmortem findings can help establish effective therapeutic strategies to reduce mortality. The aim of this study was therefore to review complete autopsy cases and their postmortem findings and comorbidities associated with death caused by COVID-19, in order to establish a profile of the deceased and the likelihood of time to death. ⋯ Given that accurate information of complete autopsies findings is still scarce, it is necessary to perform complete autopsies by examining organs other than the lungs in order to provide information to improve new treatment strategies in patients with a high risk of mortality.
-
Review
An insight into diagnosis of depression using machine learning techniques: a systematic review.
In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis. ⋯ The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.
-
Neuropsychiatric disorders in brain tumor patients are commonly observed. It is difficult to anticipate these disorders in different types of brain tumors. The goal of the study was to see how well machine learning (ML)-based decision algorithms might predict neuropsychiatric problems in different types of brain tumors. ⋯ Random Forest Trees can be used to accurately predict neuropsychiatric illnesses. Based on the model output, the ML-decision trees will aid the physician in pre-diagnosing the mental issue and deciding on the best therapeutic approach to avoid subsequent neuropsychiatric issues in brain tumor patients.