Postawy wobec palenia tytoniu wśród studentów I i VI roku medycyny z rocznika studiów 2002-2008

Wstęp: Zjawisko palenia tytoniu wśród studentów medycyny wskazuje, że studia medyczne nie stanowią wystarczającej bariery przed kontynuowaniem, a nawet rozpoczynaniem palenia. Celem badania była ocena postaw wobec palenia wśród studentów I i VI roku Wydziału Lekarskiego Gdańskiego Uniwersytetu Medycznego z rocznika studiów 2002-2008. Materiał i metody: Wśród studentów rocznika studiów 2002-2008 dwukrotnie, na I i VI roku, rozprowadzono ankietę zawierającą pytania na temat kwestii związanych z paleniem tytoniu. W ankiecie adresowanej do studentów VI roku zawarto dodatkowe pytania, umożliwiające ocenę zmian w postawach studentów wobec palenia w trakcie studiów, a także poznanie opinii respondentów na temat nauczania na studiach rozpoznawania i leczenia zespołu uzależnienia od tytoniu (ZUT) oraz ich samooceny posiadanej wiedzy w tym zakresie. W badaniu wzięło udział 287 studentów I roku i 175 studentów VI roku badanego rocznika. Wyniki: Wraz z końcem studiów studenci istotnie rzadziej regularnie palili papierosy niż na I roku (13% v. 21%; p = 0,022), jednak co piąta paląca osoba (20%) zaczęła palić papierosy w trakcie studiów medycznych. Odsetek palaczy, którzy palili bez większego skrępowania, był istotnie niższy, niż na początku studiów (31% v. 70%; p = 0,0006), stwierdzono także znacząco wyższe odsetki palaczy deklarujących chęć porzucenia nałogu (91% v. 61%; p = 0,013) oraz codziennych palaczy, którzy chcieliby poddać się leczeniu uzależnienia od tytoniu (54% v. 22%; p = 0,001). Ponad połowa studentów VI roku przyznała, że nie ma żadnej wiedzy na temat rozpoznawania i leczenia ZUT lub ich wiedza na ten temat jest bardzo słaba lub słaba (57%). Aż 43% badanych stwierdziło, że studia medyczne w ogóle nie były dla nich źródłem wiedzy o ZUT. Wnioski: Studia medyczne wpływają na pozytywne zmiany postaw studentów wobec palenia tytoniu. Jednak część osób podejmuje palenie na studiach, co sugeruje dominujący udział czynników genetycznych nad środowiskowymi w rozpoczynaniu palenia w tym okresie życia. W opinii przyszłych lekarzy studia medyczne nie są wystarczającym źródłem wiedzy o ZUT.Introduction: The prevalence of smoking among medical students indicates that studying medicine is an insufficient protection from tobacco use. The aim of the study was an analysis of medical students’ attitudes towards smoking at the first and sixth year of their studies. Material and methods: A questionnaire on tobacco smoking was distributed among medical students of the study year 2002-2008 at the first and sixth year of their studies. The questionnaire used on the sixth year students included additional questions designed to assess changes in their attitudes towards smoking during their studies, to ask their opinion of the teaching of diagnostics and treatment of tobacco dependence (TD), and to discover how they evaluated their knowledge of the issue. The numbers of students who participated at the two points of the study were 287 and 175 respectively. Results: Students in their sixth year significantly less frequently smoked cigarettes regularly than those starting their medical education (13% v. 21%; p = 0.022). However, 20% of smokers started smoking during their studies. The proportion of smokers saying they were not embarrassed by their smoking habit was significantly lower among sixth-year students compared to first-year students (31% v. 70%; p = 0.0006), as were the numbers who said they wanted to quit smoking (91% v. 61%). Those who wished to undergo treatment for TD (54% v. 22%) were significantly higher among sixth year students group (p = 0.013 and p = 0.001, respectively). More than half (57%) the sixth-year students claimed that they had no knowledge of the diagnostics and treatment of TD, or that their knowledge on this issue was poor or very poor. In the opinion of 43% of students, the medical curriculum was not a good source of knowledge on TD. Conclusions: Medical studies induce positively students’ attitudes towards smoking. However, a proportion of individuals start smoking during studies, which may suggest dominance of genetic influences on smoking initiation in this period of life. In sixth-year students’ opinion, medical studies are not a sufficient source of knowledge on TD

Coexistance of lung cancer and chronic obstructive pulmonary disease

Wstęp. Celem niniejszego opracowania było określenie częstości występowania przewlekłej obturacyjnej choroby płuc (POChP) u chorych na zaawansowanego raka płuca. Materiał i metody. Badaniem objęto kolejnych 51 chorych na zaawansowanego (stopień IIIB i IV) raka płuca - 13 kobiet (25%) i 38 mężczyzn (75%) w wieku 40-80 lat (średnia wieku: 63 lata). Wyniki. Wśród 51 chorych na zaawansowanego raka płuca w 18 przypadkach (35%) rozpoznano POChP. W tej grupie u niemal 3/4 osób (72%) występowała umiarkowana, a u 28% ciężka i bardzo ciężka postać choroby. Związek między POChP a rakiem płuca był najsilniejszy u chorych na raka płaskonabłonkowego (współczynnik korelacji Spearmana: r = 0,43; różnica w porównaniu z pozostałymi typami: p = 0,002). Wykazano, że współwystępowanie raka płuca i POChP wiązało się z większym narażeniem na dym tytoniowy, jednak zależność ta nie osiągnęła znamienności statystycznej (p = 0,072). Nie stwierdzono korelacji między obecnością POChP w badanej grupie a innymi klinicznymi cechami, takimi jak wiek, płeć i zaawansowanie choroby nowotworowej. Wnioski. Współwystępowanie raka płuca i POChP jest częste. Wydaje się, że uwzględnienie współistniejących pneumonologicznych schorzeń w opiece paliatywnej nad chorymi w końcowej fazie choroby nowotworowej pozwoliłoby poprawić jakość ich życia.Background. The aim of the study was to evaluate the frequency of coexistance of lung cancer and chronic obstructive pulmonary disease. Material and methods. Fifty one patients (13 women and 38 men, aged from 40 to 80 years, range: 63 years) with diagnosed advanced lung cancer (stage IIIB and IV) were included into the study. Results. The chronic obstructive pulmonary disease was diagnosed in 18 cases (35%), including 72% moderate and 28% with severe and very severe disease. The chronic obstructive pulmonary disease was significantly more frequent in squamous cell lung carcinoma in comparison to other subtypes (p = 0.002). There was also a tendency to coexistence of lung cancer and the chronic obstructive pulmonary disease in patients with higher exposure to cigarette smoke (p = 0.072).Conclusion. Coexistence of lung cancer and the chronic obstructive pulmonary disease is frequent, thus it is important to include treatment for the chronic obstructive pulmonary disease in palliative care of advanced lung cancer patients

Preliminary Outcomes 1 Year after Laparoscopic Sleeve Gastrectomy Based on Bariatric Analysis and Reporting Outcome System (BAROS)

# The Author(s) 2011. This article is published with open access at Springerlink.com Background The aim of this study was to assess outcomes of laparoscopic sleeve gastrectomy (LSG) as a stand-alone bariatric operation according to the Bariatric Analysis and Reporting Outcome System (BAROS). Methods Out of 112 patients included and operated on initially, 84 patients (F/M, 63:21) were followed up for 14– 56 months (mean 22±6.75). Patients lost to follow-up did not attend scheduled follow-up visits or they have withdrawn their consent. Mean age was 39 years (range 17–67; SD±12.09) with mean initial BMI 44.62 kg/m 2 (range 29.39–82.8; SD±8.17). Statistical significance was established at the p<0.05 level. Results Mean operative time was 61 min (30–140 min) with mean hospital stay of 1.37 days (0–4; SD±0.77). Excellent global BAROS outcome was achieved in 13 % of patients, very good in 30%, good in 34.5%, fair 9.5 % and failure in 13 % patients 12 months after surgery. Females achieved significantly better outcomes than males with the mean 46.5 % of excess weight loss (EWL) versus 35.3 % of EWL at 12 months (p=0.02). The mean percentage of excess weight loss (%EWL) was 43.6 % at 12 months and 46.6 % at 24 months. Major surgical complication rate was 7.1%; minor surgical complication rate 8.3%. There was one conversion (1.2%) due to the massive bleeding. Comorbidities improved or resolved in numerous patients

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI