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Objective: To characterize the clinical and histological profile, as well as treatment patterns, of patients with early-stage, locally advanced (LA), or advanced/metastatic (AM) lung cancer, diagnosed between 2000 and 2014, in Brazil. Methods: This was an analytical cross-sectional epidemiological study employing data obtained for the 2000-2014 period from the hospital cancer registries of two institutions in Brazil: the José Alencar Gomes da Silva National Cancer Institute, in the city of Rio de Janeiro; and the São Paulo Cancer Center Foundation, in the city of São Paulo. Results: We reviewed the data related to 73,167 patients with lung cancer. The proportions of patients with early-stage, LA, and AM lung cancer were 13.3%, 33.2%, and 53.4%, respectively. The patients with early-stage lung cancer were older and were most likely to receive a histological diagnosis of adenocarcinoma; the proportion of patients with early-stage lung cancer remained stable throughout the study period. In those with LA lung cancer, squamous cell carcinoma predominated, and the proportion of patients with LA lung cancer decreased significantly over the period analyzed. Those with AM lung cancer were younger and were most likely to have adenocarcinoma; the proportion of patients with AM lung cancer increased significantly during the study period. Small cell carcinoma accounted for 9.2% of all cases. In our patient sample, the main treatment modality was chemotherapy. Conclusions: It is noteworthy that the frequency of AM lung cancer increased significantly during the study period, whereas that of LA lung cancer decreased significantly and that of early-stage lung cancer remained stable. Cancer treatment patterns, by stage, were in accordance with international guidelines.

Background The importance of classifying cancer patients into high- or low-risk groups has led many research teams, from the biomedical and bioinformatics fields, to study the application of machine learning (ML) algorithms. The International Society of Geriatric Oncology recommends the use of the comprehensive geriatric assessment (CGA), a multidisciplinary tool to evaluate health domains, for the follow-up of elderly cancer patients. However, no applications of ML have been proposed using CGA to classify elderly cancer patients. Objective The aim of this study was to propose and develop predictive models, using ML and CGA, to estimate the risk of early death in elderly cancer patients. Methods The ability of ML algorithms to predict early mortality in a cohort involving 608 elderly cancer patients was evaluated. The CGA was conducted during admission by a multidisciplinary team and included the following questionnaires: mini-mental state examination (MMSE), geriatric depression scale-short form, international physical activity questionnaire-short form, timed up and go, Katz index of independence in activities of daily living, Charlson comorbidity index, Karnofsky performance scale (KPS), polypharmacy, and mini nutritional assessment-short form (MNA-SF). The 10-fold cross-validation algorithm was used to evaluate all possible combinations of these questionnaires to estimate the risk of early death, considered when occurring within 6 months of diagnosis, in a variety of ML classifiers, including Naive Bayes (NB), decision tree algorithm J48 (J48), and multilayer perceptron (MLP). On each fold of evaluation, tiebreaking is handled by choosing the smallest set of questionnaires. Results It was possible to select CGA questionnaire subsets with high predictive capacity for early death, which were either statistically similar (NB) or higher (J48 and MLP) when compared with the use of all questionnaires investigated. These results show that CGA questionnaire selection can improve accuracy rates and decrease the time spent to evaluate elderly cancer patients. Conclusions A simplified predictive model aiming to estimate the risk of early death in elderly cancer patients is proposed herein, minimally composed by the MNA-SF and KPS. We strongly recommend that these questionnaires be incorporated into regular geriatric assessment of older patients with cancer.

Objectives: to develop a flow to ensure care for all people with severe acute respiratory syndrome Coronavirus 2, offering from intensive care to palliative care, in an equitable and fair manner. Methods: the modified Delphi methodology was used to reach consensus on a flow and a prioritization index among specialists, the regional council of medicine, members of the healthcare system and the local judicial sector. Results: the score was incorporated into the flow as the final phase for building the list of patients who will be referred to intensive care, whenever a ventilator is available. Patients with lower scores should have priority access to the ICU. Patients with higher scores should receive palliative care associated with available curative measures. However, curative measures must be proportionate to the severity of the overall clinical situation and the prognosis. Conclusions: this tool could and will prevent patients from being excluded from access to the necessary health care so that their demands are assessed, their suffering is reduced, and their illnesses are cured, when possible.

Objectives: to analyze the epidemiological and clinical aspects of accidents caused by venomous animals in children under 15 years old. Methods: a cross-sectional study with an analytical component using secondary data from Centro de Informação e Assistência Toxicológica de Pernambuco (CIATox-PE), (Poison Center in Pernambuco)), in 2017 to 2019. Notifications of accidents caused were included in the studied age group and evaluated the characteristics of poisoning (animal classification, exposure zone, place and time of the occurrence and specific use of serum therapy), and of the patient (sociodemographic variables, clinical condition and evolution). The analysis performed in STATA® 13.1 presents frequency distribution tables and Pearson’s chi-square for comparison. Results: of the 2678 notifications, 82,8% were scorpionism and 10, 8% snakebite. The age group of1 to 9 years old (70.5%) and being male (54.1%) were predominant. Most of the cases occurred in urban area (80.9%), in Recife (67.3%), inside the victim’s residence (83.9%) and at night (47.3%). The majority (87.1%) were classified as ‘mild severity’, 10% received antivenom therapy and one died (by scorpionism). Two cases of snakebite in the workplace were registered. Conclusion: there was a high frequency of accidents caused in the urban area, which may be related to the lack of urban planning and sanitary education. The accidents caused among children in the household environment and the suspicion of child labor in the age group of 10 to 14 years old were also highlighted which favors the development and habits of the venomous animal.