Little is known concerning the specific clinical characteristics of patients in persistent vegetative states (PVS). Fifty-one patients from four nursing homes, approximately 3% of the total patients, were identified as being in a PVS. The mean age of the patients was 64.8 +/- 3.2 years (range, 19 to 96 years) and the mean duration of the PVS was 3.3 +/- 5.0 years (range, 1 to 16.8 years), with 13 patients' PVS being longer than 5 years. Cerebrovascular accidents and dementia were the most common causes of the PVS, accounting for 32 of the cases (63%). In the younger patients cerebral trauma secondary to motor vehicle accidents was the most common cause. All 51 patients were fed via tube feeding and 35 patients had urinary catheters (75%). All patients were receiving daily medications, with greater than 50% taking daily vitamins. Over 30% were taking digitalis and/or diuretics and over 32% were taking H2 blockers. Transfer of patients to an acute care hospital was not uncommon, with 31 patients (61%) requiring 63 acute care hospitalizations during their stay in the nursing home. As expected, infections were the most common reason for acute care hospitalization, although 15 of the patients were hospitalized for surgical procedures. Another common problem encountered by the patients was pressure sores, with 78% of patients requiring specific therapy for at least one pressure sore. Surprisingly, only 27 (53%) of 51 patients had a specific resuscitation status designation in the medical chart, and neither presence of a chart designation nor specific resuscitation order was related to the patient's age or the cause or duration of PVS. From these data it would appear that clinical characteristics of patients in PVS are variable. Some patients are young, others are old. The cause varies from cerebrovascular accidents to cerebral trauma. Survival may be prolonged; complications are not uncommon, with some patients requiring acute care hospitalization.
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Clinics (Sao Paulo)
January 2025
Department of Hematology, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, PR China. Electronic address:
Background: The common drugs used for the treatment of Newly Diagnosed Multiple Myeloma (NDMM) include bortezomib and lenalidomide, but the adverse effects of lenalidomide cannot be ignored, especially when it is used in the initial therapy.
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Clinics (Sao Paulo)
January 2025
Department of Otolaryngology and Head and Neck Surgery, The First Affiliated Hospital of Bengbu Medical College, Anhui Province, China. Electronic address:
Objective: TRIB3 has been confirmed to participate in and regulate biological metabolic activities in head and neck tumors such as nasopharyngeal carcinoma and oropharyngeal carcinoma, so the purpose of this study was to explore whether there is a correlation between TRIB3 and Laryngeal Squamous Cell Carcinoma (LSCC) and to preliminarily explore the biological characteristics of TRIB3 in LSCC.
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Ann Intern Med
January 2025
Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California (A.B., K.J.C., A.A.K.).
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Ann Intern Med
January 2025
Renal-Electrolyte Division, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (M.C.-P., R.B.M., C.M.P.).
Background: Prior studies indicate that 1% to 4% of Epstein-Barr virus (EBV)-seronegative recipients of EBV-seropositive donor (EBV D+/R-) kidneys develop posttransplant lymphoproliferative disorder (PTLD). However, these estimates are based on limited data that lack granularity.
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Ann Intern Med
January 2025
Clinical Epidemiology and Research Center (CERC), Department of Biomedical Sciences, Humanitas University, and IRCCS Humanitas Research Hospital, Milan, Italy, and Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and Immunology, Berlin, Germany (H.J.S.).
Description: Artificial intelligence (AI) has been defined by the High-Level Expert Group on AI of the European Commission as "systems that display intelligent behaviour by analysing their environment and taking actions-with some degree of autonomy-to achieve specific goals." Artificial intelligence has the potential to support guideline planning, development and adaptation, reporting, implementation, impact evaluation, certification, and appraisal of recommendations, which we will refer to as "guideline enterprise." Considering this potential, as well as the lack of guidance for the use of AI in guidelines, the Guidelines International Network (GIN) proposes a set of principles for the development and use of AI tools or processes to support the health guideline enterprise.
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