In forensic anthropology, ancestry estimation is essential in establishing the individual biological profile. The aim of this study is to present a new program--AncesTrees--developed for assessing ancestry based on metric analysis. AncesTrees relies on a machine learning ensemble algorithm, random forest, to classify the human skull. In the ensemble learning paradigm, several models are generated and co-jointly used to arrive at the final decision. The random forest algorithm creates ensembles of decision trees classifiers, a non-linear and non-parametric classification technique. The database used in AncesTrees is composed by 23 craniometric variables from 1,734 individuals, representative of six major ancestral groups and selected from the Howells' craniometric series. The program was tested in 128 adult crania from the following collections: the African slaves' skeletal collection of Valle da Gafaria; the Medical School Skull Collection and the Identified Skeletal Collection of 21st Century, both curated at the University of Coimbra. The first step of the test analysis was to perform ancestry estimation including all the ancestral groups of the database. The second stage of our test analysis was to conduct ancestry estimation including only the European and the African ancestral groups. In the first test analysis, 75% of the individuals of African ancestry and 79.2% of the individuals of European ancestry were correctly identified. The model involving only African and European ancestral groups had a better performance: 93.8% of all individuals were correctly classified. The obtained results show that AncesTrees can be a valuable tool in forensic anthropology.
The purpose of this study is to characterize and contextualize the new collection of This collection constitutes a fundamental tool for forensic anthropology research, including development and validation studies of skeletal aging and sexing methods that target elderly adults. Moreover, this collection can also be used in conjunction with the other reference collections housed in the University of Coimbra to investigate secular trends in skeletal development and aging, among others.
Sex estimation is extremely important in the analysis of human remains as many of the subsequent biological parameters are sex specific (e.g., age at death, stature, and ancestry). When dealing with incomplete or fragmented remains, metric analysis of the tarsal bones of the feet has proven valuable. In this study, the utility of 18 width, length, and height tarsal measurements were assessed for sex-related variation in a Portuguese sample. A total of 300 males and females from the Coimbra Identified Skeletal Collection were used to develop sex prediction models based on statistical and machine learning algorithm such as discriminant function analysis, logistic regression, classification trees, and artificial neural networks. All models were evaluated using 10-fold cross-validation and an independent test sample composed of 60 males and females from the Identified Skeletal Collection of the 21st Century. Results showed that tarsal bone sex-related variation can be easily captured with a high degree of repeatability. A simple tree-based multivariate algorithm involving measurements from the calcaneus, talus, first and third cuneiforms, and cuboid resulted in 88.3% correct sex estimation both on training and independent test sets. Traditional statistical classifiers such as the discriminant function analysis were outperformed by machine learning techniques. Results obtained show that machine learning algorithm are an important tool the forensic practitioners should consider when developing new standards for sex estimation.
Background In patients with peripheral intravenous catheters (PIVCs), performing flushing is an essential procedure to maintain catheter patency and prevent complications. These PIVC related complications can lead to premature removal and therapeutics interruption, which implies the need of a new catheterization thus increasing patient discomfort and pain. Aims To identify nursing practices related to the flushing procedure, namely: moment(s) of the flushing; the syringe size used; the flush solution, volume and technique; the knowledge and accomplishment of the recommended standards on flushing by nurses. Methods A cross-sectional study was conducted between July and December 2017, with Brazilian and Portuguese nurses. An online questionnaire was developed based on the international recommendations on flushing procedure. Descriptive analysis was performed. Results A total of 76 nurses answered the questionnaire. The majority of nurses (84.2%) performed flushing: the most common technique used was continuous syringe pressure (31.2%), with the push-pause technique being performed by 23.4% of the nurses. Despite the majority performs flushing at four distinct moments (after the PIVC insertion, before, between and after drug delivery), there are inconsistencies in flush solution, volume, and syringe size. The most used volume to perform flushing was 5 mL, filled using normal saline. Despite this, they also recognized the omission of this procedure due to time constrains, no familiarity with the procedure and unavailable material. Conclusions This study identified that flushing procedure isn't always performed by nurses in their clinical practice. Also, several inconsistencies were observed between nurses that performed flushing, reflecting the lack of empirical evidence in this area of research.
BackgroundRecurrent use of oral corticosteroids (OCS) and over-use of short-acting beta-2-agonists (SABA) are factors associated with adverse side effects and asthma-related death. We aim to quantify high OCS exposure, SABA over-use and its association with prescription and adherence to maintenance treatment for respiratory disease, among patients with prescriptions for respiratory disease, from the Portuguese electronic prescription and dispensing database (BDNP).MethodsThis was a 1-year (2016) retrospective population-based analysis of a random sample of adult patients from the BDNP, the nationwide compulsory medication prescription system. We assessed high OCS exposure (dispensing ≥ 4 packages containing 20 doses of 20 mg each of prednisolone-equivalent, ≥ 1600 mg/year) on patients on persistent respiratory treatment (PRT-prescription for > 2 packages of any respiratory maintenance medications). Excessive use of SABA was defined as having a ratio of SABA-to-maintenance treatment > 1 or having SABA over-use (dispensing of > 1 × 200 dose canister/month, of 100 μg of salbutamol-equivalent). Factors associated with high OCS exposure were assessed by multinomial logistic regression.ResultsThe estimated number of patients on PRT was 4786/100,000 patients. OCS was prescribed to more than 1/5 of the patients on PRT and 101/100,000 were exposed to a high-dose (≥ 1600 mg/year). SABA excessive use was found in 144/100,000 patients and SABA over-use in 24/100,000. About 1/6 of SABA over-users were not prescribed any controller medication and 7% of them had a ratio maintenance-to-total ≥ 70% (high prescription of maintenance treatment). Primary adherence (median%) to controller medication was 66.7% for PRT patients, 59.6% for patients exposed to high OCS dose and 75.0% for SABA over-users. High OCS exposure or SABA over-use were not associated with primary adherence. High OCS exposure was associated with a maintenance-to-total medication ratio < 70% (insufficient prescription of maintenance treatment), age > 45 years old and male sex.ConclusionsExposure to high-dose of OCS (101 per 100,000 patients) and SABA over-use (24 per 100,000) were frequent, and were associated with a low maintenance-to-total prescription ratio but not with primary non-adherence. These results suggest there is a need for initiatives to reduce OCS and SABA inappropriate prescribing.
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