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Background Given the rapid growth of the global aging population, pain has become an unneglectable concern amongst the elderly. The quantity of scientific research outputs on pain in the elderly has increased over time, but only a small number of studies have used bibliometric methods to analyze scientific research in this field. This paper aimed to analyze scientific research on pain in the elderly published from 2000 to 2019 in a systematic manner using bibliometric methods. Methods Articles on pain in the elderly published from 2000 to 2019 were retrieved from the Web of Science (WoS). Abstracts were coded on the basis of predetermined items (eg, type of article, topic, type of subjects, pain characteristics), and relevant information on the first author, citation scores, and article keywords were collected. Results A total of 2105 articles were included in this study. Statistical analysis revealed that the publication of articles on pain in the elderly increased in frequency over time (P<0.001). Most of the publications were original articles. Amongst the countries identified, the United States published the largest number of papers on this topic. Pain characteristics (50.21%), pain intervention (35.68%), and pain assessment (9.69%) were the main topics of research on geriatric pain. Back pain (12.30%) appeared to be the most popular pain type described in the included papers. Conclusion This work provides researchers with an in-depth understanding of pain in the elderly by evaluating relevant publications in the past two decades. Researchers in this field are warranted to explore future directions on geriatric pain such as the transition from acute pain to chronic pain and the underlying mechanisms of pain in the elderly.

Background: Screening for post-stroke cognitive impairment (PSCI) is necessary because stroke increases the incidence of and accelerates premorbid cognitive decline. The Quick Mild Cognitive Impairment (Qmci) screen is a short, reliable and accurate cognitive screening instrument but is not yet validated in PSCI. We compared the diagnostic accuracy of a Chinese version of the Qmci screen (Qmci-CN) compared with the widely-used Chinese versions of the Montreal Cognitive Assessment (MoCA-CN) and Mini-Mental State Examination (MMSE-CN).Methods: We recruited 34 patients who had recovered from a stroke in rehabilitation unit clinics in 2 university hospitals in China: 11 with post-stroke dementia (PSD), 15 with post-stroke cognitive impairment no dementia (PSCIND), and 8 with normal cognition (NC). Classification was made based on clinician assessment supported by a neuropsychological battery, independent of the screening test scores. The Qmci-CN, MoCA-CN, and MMSE-CN screens were administered randomly by a trained rater, blind to the diagnosis.Results: The mean age of the sample was 63 ± 13 years and 61.8% were male. The Qmci-CN had statistically similar diagnostic accuracy in differentiating PSD from NC, an area under the curve (AUC) of 0.94 compared to 0.99 for the MoCA-CN (p = 0.237) and 0.99 for the MMSE-CN (p = 0.293). The Qmci-CN (AUC 0.91), MoCA-CN (AUC 0.94), and MMSE-CN (AUC 0.79) also had statistically similar accuracy in separating PSD from PSCIND. The MoCA-CN more accurately distinguished between PSCIND and normal cognition than the Qmci-CN (p = 0.015). Compared to the MoCA-CN, the administration times of the Qmci-CN (329s vs. 611s, respectively, p &lt; 0.0001) and MMSE-CN (280 vs. 611s, respectively, p &lt; 0.0001) were significantly shorter.Conclusion: The Qmci-CN is accurate in identifying PSD and separating PSD from PSCIND in patients post-stroke following rehabilitation and is comparable to the widely-used MoCA-CN, albeit with a significantly shorter administration time. The Qmci-CN had relatively poor accuracy in identifying PSCIND from NC and hence may lack accuracy for certain subgroups. However, given the small sample size, the study is under-powered to show superiority of one instrument over another. Further study is needed to confirm these findings in a larger sample size and in other settings (countries and languages).