Bone histology was quantitated in 10 osteoporotic patients aged between 17 and 51 years and in six healthy subjects aged between 23 and 43 years. The osteoporosis was of varying aetiology and was clinically stable. All patients were given tetracycline before biopsy and double tetracycline labelling was used in seven patients. Bone forming and resorbing surfaces were defined by the presence of osteoblasts and osteoclasts, respectively, which were identified by histochemical techniques. The associations between bone forming and resorbing surfaces were similar in patients and controls, though the range of values was wider in the patients than in the controls. Mineral apposition rate was normal in the osteoporotic patients, but there was a reduction in mineralising (tetracycline) surface, whether related to osteoid surface or to osteoblast surface. This did not indicate osteomalacia as the directly and indirectly measured mineralisation lag times were normal. The osteoid seams were thinner in osteoporotic patients than in controls. The data suggest that osteoclast and osteoblast numbers were normal in this group of osteoporotic patients but that the metabolic activity of osteoblasts was impaired.
Simulation emerges as an important technique in recent years for modeling complex operational dynamics in various healthcare institutions and hence providing deep insights for potential improvement. In particular, Accident and Emergency Department (A&ED) has been a place for such research as it accounts for a large proportion of the total hospital visits and admissions. To create a viable simulation for A&ED, accurate description and forecast of patient visits is the foremost step. This paper investigates several contributing factors to A&ED visits, and various time-series methods of modeling A&ED visits with different triage categories and mode of arrival. All the methods are compared in terms of goodness-of-fit and forecast accuracy. The purpose of this research is two-fold. First, this research is part of attempt to build a simulation model for A&ED of a local hospital. Second, the results may be useful for reexamine the resource allocation plan of the A&ED.
Our dataset contains 478,175 named entities related to medication errors and also differentiates between incident types by recognising discrepancies between what was intended and what actually occurred. To use this dataset, one should also cite this original data source: Medical Adverse Event Information Collection Project [Iryō jiko jōhō shūshū-tō jigyō] Japan Council for Quality Health Care; 2022 [Available from: https://www.med-safe.jp/index.html.
SUMMARY Phenomenological and mechanistic models are widely used to assist resource planning for pandemics and emerging infections. We conducted a systematic review, to compare methods and outputs of published phenomenological and mechanistic modelling studies pertaining to the 2013–2016 Ebola virus disease (EVD) epidemics in four West African countries – Sierra Leone, Liberia, Guinea and Nigeria. We searched Pubmed, Embase and Scopus databases for relevant English language publications up to December 2015. Of the 874 articles identified, 41 met our inclusion criteria. We evaluated these selected studies based on: the sources of the case data used, and modelling approaches, compartments used, population mixing assumptions, model fitting and calibration approaches, sensitivity analysis used and data bias considerations. We synthesised results of the estimated epidemiological parameters: basic reproductive number ( R 0 ), serial interval, latent period, infectious period and case fatality rate, and examined their relationships. The median of the estimated mean R 0 values were between 1·30 and 1·84 in Sierra Leone, Liberia and Guinea. Much higher R 0 value of 9·01 was described for Nigeria. We investigated several issues with uncertainty around EVD modes of transmission, and unknown observation biases from early reported case data. We found that epidemic models offered R 0 mean estimates which are country-specific, but these estimates are not associating with the use of several key disease parameters within the plausible ranges. We find simple models generally yielded similar estimates of R 0 compared with more complex models. Models that accounted for data uncertainty issues have offered a higher case forecast compared with actual case observation. Simple model which offers transparency to public health policy makers could play a critical role for advising rapid policy decisions under an epidemic emergency.
Electronic health (eHealth) refers to the use of information and communication technologies for health. It plays an increasingly important role in patients' medication management.To assess evidence on (1) whether eHealth for patients' medication management can improve drug adherence and health outcomes in nonhospital settings and (2) which eHealth functions are commonly used and are effective in improving drug adherence.We searched for randomized controlled trials (RCTs) on PubMed, MEDLINE, CINAHL, EMBASE, EmCare, ProQuest, Scopus, Web of Science, ScienceDirect, and IEEE Xplore, in addition to other published sources between 2000 and 2018. We evaluated the studies against the primary outcome measure of medication adherence and multiple secondary health care outcome measures relating to adverse events, quality of life, patient satisfaction, and health expenditure. The quality of the studies included was assessed using the Cochrane Collaboration's Risk of Bias (RoB) tool.Our initial search yielded 9909 records, and 24 studies met the selection criteria. Of these, 13 indicated improvement in medication adherence at the significance level of P<.1 and 2 indicated an improved quality of life at the significance level of P<.01. The top 3 functions that were employed included mechanisms to communicate with caregivers, monitoring health features, and reminders and alerts. eHealth functions of providing information and education, and dispensing treatment and administration support tended to favor improved medication adherence outcomes (Fisher exact test, P=.02). There were differences in the characteristics of the study population, intervention design, functionality provided, reporting adherence, and outcome measures among the included studies. RoB assessment items, including blinding of outcome assessment and method for allocation concealment, were not explicitly reported in a large number of studies.All the studies included were designed for patient home-based care application and provided a mechanism to communicate with caregivers. A targeted study population such as older patients should be considered to maximize the public health impact on the self-management of diseases.International Prospective Register of Systematic Reviews (PROSPERO) CRD42018096627; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=96627.
Medication errors often occurred due to the breach of medication rights that are the right patient, the right drug, the right time, the right dose and the right route. The aim of this study was to develop a medication-rights detection system using natural language processing and deep neural networks to automate medication-incident identification using free-text incident reports. We assessed the performance of deep neural network models in classifying the Advanced Incident Reporting System reports and compared the models’ performance with that of other common classification methods (including logistic regression, support vector machines and the decision-tree method). We also evaluated the effects on prediction outcomes of several deep neural network model settings, including number of layers, number of neurons and activation regularisation functions. The accuracy of the models was measured at 0.9 or above across model settings and algorithms. The average values obtained for accuracy and area under the curve were 0.940 (standard deviation: 0.011) and 0.911 (standard deviation: 0.019), respectively. It is shown that deep neural network models were more accurate than the other classifiers across all of the tested class labels (including wrong patient, wrong drug, wrong time, wrong dose and wrong route). The deep neural network method outperformed other binary classifiers and our default base case model, and parameter arguments setting generally performed well for the five medication-rights datasets. The medication-rights detection system developed in this study successfully uses a natural language processing and deep-learning approach to classify patient-safety incidents using the Advanced Incident Reporting System reports, which may be transferable to other mandatory and voluntary incident reporting systems worldwide.
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