Automated Deductive Content Analysis of Text: A Deep Contrastive and Active Learning Based Approach

Content analysis traditionally involves human coders manually combing through text documents to search for relevant concepts and categories. However, this approach is time-intensive and not scalable, particularly for secondary data like social media content, news articles, or corporate reports. To address this problem, the paper presents an automated framework called Automated Deductive Content Analysis of Text (ADCAT) that uses deep learning-based semantic techniques, ontology of validated construct measures, large language model, human-in-the-loop disambiguation, and a novel augmentation-based weighted contrastive learning approach for improved language representations, to build a scalable approach for deductive content analysis. We demonstrate the effectiveness of the proposed approach to identify firm innovation strategies from their 10-K reports to obtain inferences reasonably close to human coding

Hybrid computational model to assist in the location of victims buried in the tragedy of Brumadinho

The rupture of dams in Brazil has caused great concern due to the environmental disaster and the loss of lives. The use of algorithms and computational models to assist search teams in locating victims when buried by tailings is essential but scarce. Those that exist are mainly slow, as they involve high computational costs. In this sense, in the context of the Brumadinho tragedy in 2019, this study aimed to develop a hybrid computational model to assist the search teams in locating victims buried by the tailings. The methodology for designing this model was based on regression techniques, machine learning, and physicomathematical algorithms. Firstly, the study resulted in a physicomathematical model based on integral and vector calculus and concepts of fluid mechanics, which provided results to assist in locating bodies buried by the tailings. More recently, based on data provided by the physicomathematical algorithm, two hybrid models have been developed. One uses statistical regression, and the other uses support vector regression, a type of machine learning. It is expected that a more accurate model can be used in other possible situations of disruption in future studies. Also, it is possible to apply the model developed in situations involving computational fluid dynamics in general.This paper presents a hybrid computational model  based on regression techniques, machine learning and  physicomathematical algorithms developed for assistance in locating victims in the Brumadinho tragedy in 2019. The physicomathematical model, which provided results to help search teams, is based on integral and vector calculus, and fluid mechanics concepts. In addition, from data provided by the physicomathematical algorithm, two hybrid model were developed. One of them uses regression statistical and the  other one uses support vector regression which is a type of machine learning. With good prospects of the advances in research, it is  expected in future work, a more accurate model that can be used in other possible situations of dam-break. Moreover the model can be applied to situations involving computational fluid dynamics in genera

Comparison of the anesthetic latency of articaine, lidocaine, levobupivacaine and ropivacaine, through Pulp Tester

INTRODUCTION : A thorough knowledge of dental anesthetics, like the lag time of the drug, can ensure the success of pain control during and after surgery.OBJECTIVE : Comparing the latency time of 4 anesthetics, in other words, the time between the deposition of the local anesthetic and when its effects become noticeable. However, this is not related with the successful pain control during and after surgery (depth of anesthesia).MATERIAL AND METHOD : We conducted a double-blind, crossover, randomized study with 30 volunteers underwent 04 procedures in one-week intervals from the posterior superior alveolar block. In the second molar being treated, the pulp tester was used at intervals of 02 minutes, considering the insensitivity of the pulp when there is no response after two consecutive tests of 80muV, peaking at 10 minutes thus determining the latency period of the anesthetic. The data were submitted to t-student test, Friedman test and Kruskal-Wallis test (p&lt;0.05).RESULT : There were no statistically significant differences (p=0.8327) between the anesthetics. In all cases the median was 2 minutes. Still, there were no significant differences between genders in relation to age (p=0.4545), as well as between the values when it attempted to observe the influence of gender in latency values (p=0.6754).CONCLUSION : Since the average lag time was identical, the choice of the drug will depend on the duration of the oral surgery and the necessity of postoperative analgesia.Um conhecimento profundo dos anestésicos odontológicos, como o tempo de latência da droga, pode assegurar o êxito do controle da dor no trans e no pós-operatório. Comparar a latência entre quatro soluções anestésicas, ou seja, o tempo entre o início da deposição do anestésico local e o momento em que seus efeitos tornam-se perceptíveis. Entretanto, isso não está relacionado com o êxito do controle da dor no trans e no pós-operatório (profundidade da anestesia). Foi realizado um estudo duplo cego, cruzado e randomizado, com 30 pacientes voluntários submetidos a quatro procedimentos em intervalos de uma semana, a partir de bloqueio do alveolar superior posterior. No segundo molar a ser tratado, foi utilizado o 'pulp tester' em intervalos de 2 minutos, considerando a insensibilidade da polpa quando da ausência de resposta após dois testes consecutivos de 80muV, chegando ao máximo de 10 minutos e determinando, assim, o período de latência do anestésico. Os dados foram submetidos aos testes T-student, de Friedman e de Kruskal-Wallis (p<0,05). Não houve diferenças estatisticamente significativas (p=0,8327) entre as soluções anestésicas. Para todas estas, a mediana foi 2 minutos. Não houve, ainda, diferenças significantes entre os gêneros em relação à idade (p=0,4545), bem como entre os valores, quando se tentou observar a influência do gênero nos valores de latência (p=0,6754). Sendo os tempos médios de latência idênticos, a escolha da droga dependerá da duração do procedimento cirúrgico-odontológico a se realizar, além da necessidade ou não de analgesia pós-operatória431814A thorough knowledge of dental anesthetics, like the lag time of the drug, can ensure the success of pain control during and after surgery. Comparing the latency time of 4 anesthetics, in other words, the time between the deposition of the local anesthetic and when its effects become noticeable. However, this is not related with the successful pain control during and after surgery (depth of anesthesia). We conducted a double-blind, crossover, randomized study with 30 volunteers underwent 04 procedures in one-week intervals from the posterior superior alveolar block. In the second molar being treated, the pulp tester was used at intervals of 02 minutes, considering the insensitivity of the pulp when there is no response after two consecutive tests of 80muV, peaking at 10 minutes thus determining the latency period of the anesthetic. The data were submitted to t-student test, Friedman test and Kruskal-Wallis test (p&lt;0.05). There were no statistically significant differences (p=0.8327) between the anesthetics. In all cases the median was 2 minutes. Still, there were no significant differences between genders in relation to age (p=0.4545), as well as between the values when it attempted to observe the influence of gender in latency values (p=0.6754). Since the average lag time was identical, the choice of the drug will depend on the duration of the oral surgery and the necessity of postoperative analgesi

Automated Coding and Scoring of Text: Artifact Design, Application, and Evaluation

This paper presents an artifact to automate and quantify qualitative coding of text documents on predefined constructs of interest. The quantified codes, similar to Likert-scaled measures in survey research, can be analyzed using statistical models. Our approach employs sentence transformers, a deep learning model to estimate semantic text similarity between text documents and predefined construct operationalizations. We demonstrate an application of our artifact by coding and scoring a sample of corporate 10-K reports for two types of organizational innovation processes: exploration and exploitation. Our artifact is a significant methodological contribution beyond manual coding of text documents, which cannot scale up to thousands or millions of documents, or word-based automated coding, which cannot capture the semantic meaning of text and cannot score text documents. Our approach offers new possibilities in mixed-mode research by integrating qualitative and quantitative methods using design science research

Comparação da latência anestésica de Articaína, Lidocaína, Levobupivacaína e Ropivacaína através de 'Pulp Tester'

INTRODUÇÃO: Um conhecimento profundo dos anestésicos odontológicos, como o tempo de latência da droga, pode assegurar o êxito do controle da dor no trans e no pós-operatório. OBJETIVO: Comparar a latência entre quatro soluções anestésicas, ou seja, o tempo entre o início da deposição do anestésico local e o momento em que seus efeitos tornam-se perceptíveis. Entretanto, isso não está relacionado com o êxito do controle da dor no trans e no pós-operatório (profundidade da anestesia). MATERIAL E MÉTODO: Foi realizado um estudo duplo cego, cruzado e randomizado, com 30 pacientes voluntários submetidos a quatro procedimentos em intervalos de uma semana, a partir de bloqueio do alveolar superior posterior. No segundo molar a ser tratado, foi utilizado o 'pulp tester' em intervalos de 2 minutos, considerando a insensibilidade da polpa quando da ausência de resposta após dois testes consecutivos de 80muV, chegando ao máximo de 10 minutos e determinando, assim, o período de latência do anestésico. Os dados foram submetidos aos testes T-student, de Friedman e de Kruskal-Wallis (p<0,05). RESULTADO: Não houve diferenças estatisticamente significativas (p=0,8327) entre as soluções anestésicas. Para todas estas, a mediana foi 2 minutos. Não houve, ainda, diferenças significantes entre os gêneros em relação à idade (p=0,4545), bem como entre os valores, quando se tentou observar a influência do gênero nos valores de latência (p=0,6754). CONCLUSÃO: Sendo os tempos médios de latência idênticos, a escolha da droga dependerá da duração do procedimento cirúrgico-odontológico a se realizar, além da necessidade ou não de analgesia pós-operatória

Riqueza, abundância e diversidade de Euglossina (Hymenoptera, Apidae) em três áreas da Reserva Biológica Guaribas, Paraíba, Brasil Richness, abundance, and diversity of Euglossina (Hymenoptera, Apidae) at three areas of the Guaribas Biological Reserve, Paraíba, Brazil

O estudo realizou-se em três áreas da Reserva Biológica Guaribas, uma área com vegetação característica de Mata Atlântica, outra com vegetação característica de Tabuleiro Nordestino e a terceira, chamada de Transição, formada por um mosaico dos dois tipos vegetacionais. Foram coletados 2314 indivíduos pertencentes a 11 espécies de Euglossina. Utilizou-se como iscas seis fragrâncias artificiais: eugenol, eucaliptol, escatol, beta ionona, acetato de benzila e vanilina. Na área de Mata, foram coletados 850 indivíduos de 11 espécies, na área de Tabuleiro 737 indivíduos de cinco espécies e na área de Transição 727 indivíduos de seis espécies. A área de Mata apresentou a maior diversidade (H' = 0,94) e a maior riqueza. O coeficiente de similaridade binário de Sørensen indicou que as áreas mais semelhantes, com relação à composição das espécies, foram às de Tabuleiro e Transição (Ss = 0,92). O coeficiente de similaridade de Morisita apontou que as áreas de Mata e Transição são idênticas (Cmh = 1), com relação às abundâncias relativas das espécies. A área de Transição assemelha-se mais à área de Tabuleiro (quanto à composição e diversidade) e mais à área de Mata (quanto à abundância relativa), o que sugere que algumas espécies de Mata também forrageiam na área de Transição.<br>The study was carried out at three areas of the Guaribas Biological Reserve, one area with typical Atlantic rain forest vegetation, one with a Savanna-like vegetation typical of coastal ecosystems, locally known as 'Tabuleiro', and another called Transition area, containing a mosaic of the two former types of vegetation. A total of 2314 individuals belonging to 11 species of Euglossina were sampled, using traps. Six artificial fragrances were used as baits: eugenol, cineol, skatol, beta ionone, benzyl acetate, and vanillin. From the Forest area 850 males belonging to 11 species were sampled, from the Savanna-like vegetation 737 males belonging to five species were sampled, and from the Transition area 727 males belonging to six species were sampled. The highest diversity (H' = 0.94) and richness were obtained from the Forest area. The Sørensen binary similarity coefficient showed that regarding species composition Savanna-like vegetation and Transition were the most similar areas (Ss = 0.92). The Morisita similarity coefficient showed that Forest and Transition areas were identical (Cmh = 1) regarding relative abundance of species. Transition area is more similar to an open area of Savanna-like vegetation, in terms of composition and diversity, and more similar to the Forest area, regarding relative abundance, suggesting that some Forest species also forage in the Transition area

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P &lt; 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)