BACKGROUND: Perioral dermatitis (POD) is a common inflammatory skin disease without standard therapy. OBJECTIVE: We sought to evaluate the clinical value of a soothing fluid for the treatment of POD. METHODS: We included 51 patients with POD in this 8-week clinical trial. The Toleriane Fluide Efficacy in Perioral Dermatitis (TOLPOD) study had an open-label design and involved twice-daily application of Toleriane Fluide, a soothing cosmetic fluid. Clinical assessment of POD was performed with a predefined questionnaire including the POD severity index (PODSI). Control visits were made after 4 and 8 weeks of treatment. RESULTS: The results were compared with those of a historical control group treated with a vehicle cream. Patients treated with the soothing fluid showed a continuous and significant improvement of the PODSI over time. The improvement of PODSI observed with the soothing fluid was better, but not significantly better, than that observed in the historical controls. In addition, the subjective complaints of patients such as disease burden, itching, distension of the skin, and appearance improved during treatment. CONCLUSION: A soothing fluid could be a clinically useful treatment option for POD.
Dermatitis, Perioral* ; Humans ; Pruritus ; Skin ; Skin Diseases ; Surveys and Questionnaires

Objective: In the digital age, patients turn to online sources for lumbar spine fusion information, necessitating a careful study of large language models (LLMs) like chat generative pre-trained transformer (ChatGPT) for patient education. Methods: Our study aims to assess the response quality of Open AI (artificial intelligence)’s ChatGPT 3.5 and Google’s Bard to patient questions on lumbar spine fusion surgery. We identified 10 critical questions from 158 frequently asked ones via Google search, which were then presented to both chatbots. Five blinded spine surgeons rated the responses on a 4-point scale from ‘unsatisfactory’ to ‘excellent.’ The clarity and professionalism of the answers were also evaluated using a 5-point Likert scale. Results: In our evaluation of 10 questions across ChatGPT 3.5 and Bard, 97% of responses were rated as excellent or satisfactory. Specifically, ChatGPT had 62% excellent and 32% minimally clarifying responses, with only 6% needing moderate or substantial clarification. Bard’s responses were 66% excellent and 24% minimally clarifying, with 10% requiring more clarification. No significant difference was found in the overall rating distribution between the 2 models. Both struggled with 3 specific questions regarding surgical risks, success rates, and selection of surgical approaches (Q3, Q4, and Q5). Interrater reliability was low for both models (ChatGPT: k = 0.041, p = 0.622; Bard: k = -0.040, p = 0.601). While both scored well on understanding and empathy, Bard received marginally lower ratings in empathy and professionalism. Conclusion ChatGPT3.5 and Bard effectively answered lumbar spine fusion FAQs, but further training and research are needed to solidify LLMs’ role in medical education and healthcare communication.

Objective: In the digital age, patients turn to online sources for lumbar spine fusion information, necessitating a careful study of large language models (LLMs) like chat generative pre-trained transformer (ChatGPT) for patient education. Methods: Our study aims to assess the response quality of Open AI (artificial intelligence)’s ChatGPT 3.5 and Google’s Bard to patient questions on lumbar spine fusion surgery. We identified 10 critical questions from 158 frequently asked ones via Google search, which were then presented to both chatbots. Five blinded spine surgeons rated the responses on a 4-point scale from ‘unsatisfactory’ to ‘excellent.’ The clarity and professionalism of the answers were also evaluated using a 5-point Likert scale. Results: In our evaluation of 10 questions across ChatGPT 3.5 and Bard, 97% of responses were rated as excellent or satisfactory. Specifically, ChatGPT had 62% excellent and 32% minimally clarifying responses, with only 6% needing moderate or substantial clarification. Bard’s responses were 66% excellent and 24% minimally clarifying, with 10% requiring more clarification. No significant difference was found in the overall rating distribution between the 2 models. Both struggled with 3 specific questions regarding surgical risks, success rates, and selection of surgical approaches (Q3, Q4, and Q5). Interrater reliability was low for both models (ChatGPT: k = 0.041, p = 0.622; Bard: k = -0.040, p = 0.601). While both scored well on understanding and empathy, Bard received marginally lower ratings in empathy and professionalism. Conclusion ChatGPT3.5 and Bard effectively answered lumbar spine fusion FAQs, but further training and research are needed to solidify LLMs’ role in medical education and healthcare communication.

Objective: In the digital age, patients turn to online sources for lumbar spine fusion information, necessitating a careful study of large language models (LLMs) like chat generative pre-trained transformer (ChatGPT) for patient education. Methods: Our study aims to assess the response quality of Open AI (artificial intelligence)’s ChatGPT 3.5 and Google’s Bard to patient questions on lumbar spine fusion surgery. We identified 10 critical questions from 158 frequently asked ones via Google search, which were then presented to both chatbots. Five blinded spine surgeons rated the responses on a 4-point scale from ‘unsatisfactory’ to ‘excellent.’ The clarity and professionalism of the answers were also evaluated using a 5-point Likert scale. Results: In our evaluation of 10 questions across ChatGPT 3.5 and Bard, 97% of responses were rated as excellent or satisfactory. Specifically, ChatGPT had 62% excellent and 32% minimally clarifying responses, with only 6% needing moderate or substantial clarification. Bard’s responses were 66% excellent and 24% minimally clarifying, with 10% requiring more clarification. No significant difference was found in the overall rating distribution between the 2 models. Both struggled with 3 specific questions regarding surgical risks, success rates, and selection of surgical approaches (Q3, Q4, and Q5). Interrater reliability was low for both models (ChatGPT: k = 0.041, p = 0.622; Bard: k = -0.040, p = 0.601). While both scored well on understanding and empathy, Bard received marginally lower ratings in empathy and professionalism. Conclusion ChatGPT3.5 and Bard effectively answered lumbar spine fusion FAQs, but further training and research are needed to solidify LLMs’ role in medical education and healthcare communication.

Objective: In the digital age, patients turn to online sources for lumbar spine fusion information, necessitating a careful study of large language models (LLMs) like chat generative pre-trained transformer (ChatGPT) for patient education. Methods: Our study aims to assess the response quality of Open AI (artificial intelligence)’s ChatGPT 3.5 and Google’s Bard to patient questions on lumbar spine fusion surgery. We identified 10 critical questions from 158 frequently asked ones via Google search, which were then presented to both chatbots. Five blinded spine surgeons rated the responses on a 4-point scale from ‘unsatisfactory’ to ‘excellent.’ The clarity and professionalism of the answers were also evaluated using a 5-point Likert scale. Results: In our evaluation of 10 questions across ChatGPT 3.5 and Bard, 97% of responses were rated as excellent or satisfactory. Specifically, ChatGPT had 62% excellent and 32% minimally clarifying responses, with only 6% needing moderate or substantial clarification. Bard’s responses were 66% excellent and 24% minimally clarifying, with 10% requiring more clarification. No significant difference was found in the overall rating distribution between the 2 models. Both struggled with 3 specific questions regarding surgical risks, success rates, and selection of surgical approaches (Q3, Q4, and Q5). Interrater reliability was low for both models (ChatGPT: k = 0.041, p = 0.622; Bard: k = -0.040, p = 0.601). While both scored well on understanding and empathy, Bard received marginally lower ratings in empathy and professionalism. Conclusion ChatGPT3.5 and Bard effectively answered lumbar spine fusion FAQs, but further training and research are needed to solidify LLMs’ role in medical education and healthcare communication.

Objective: In the digital age, patients turn to online sources for lumbar spine fusion information, necessitating a careful study of large language models (LLMs) like chat generative pre-trained transformer (ChatGPT) for patient education. Methods: Our study aims to assess the response quality of Open AI (artificial intelligence)’s ChatGPT 3.5 and Google’s Bard to patient questions on lumbar spine fusion surgery. We identified 10 critical questions from 158 frequently asked ones via Google search, which were then presented to both chatbots. Five blinded spine surgeons rated the responses on a 4-point scale from ‘unsatisfactory’ to ‘excellent.’ The clarity and professionalism of the answers were also evaluated using a 5-point Likert scale. Results: In our evaluation of 10 questions across ChatGPT 3.5 and Bard, 97% of responses were rated as excellent or satisfactory. Specifically, ChatGPT had 62% excellent and 32% minimally clarifying responses, with only 6% needing moderate or substantial clarification. Bard’s responses were 66% excellent and 24% minimally clarifying, with 10% requiring more clarification. No significant difference was found in the overall rating distribution between the 2 models. Both struggled with 3 specific questions regarding surgical risks, success rates, and selection of surgical approaches (Q3, Q4, and Q5). Interrater reliability was low for both models (ChatGPT: k = 0.041, p = 0.622; Bard: k = -0.040, p = 0.601). While both scored well on understanding and empathy, Bard received marginally lower ratings in empathy and professionalism. Conclusion ChatGPT3.5 and Bard effectively answered lumbar spine fusion FAQs, but further training and research are needed to solidify LLMs’ role in medical education and healthcare communication.

BACKGROUND: The level of Galectin-3 (Gal-3) protein purportedly reflects an ongoing cardiac fibrotic process and has been associated with ventricular remodeling, which is instrumental in the development of heart failure with preserved ejection fraction (HFpEF) syndrome. The aim of this study was to investigate the potential use of Gal-3 in improved characterization of the grades of diastolic dysfunction as defined by echocardiography. METHODS: Seventy HFpEF patients undergoing routine echocardiography were prospectively enrolled in the present monocentric study. Blood samples for measurements of Gal-3 and amino-terminal pro-brain natriuretic peptide (NT-proBNP) were collected within 24 hours pre- or post-echocardiographic examination. The classification of patients into subgroups based on diastolic dysfunction grade permitted detailed statistical analyses of the derived data. RESULTS: The Gal-3 serum levels of all patients corresponded to echocardiographic indices, suggesting HFpEF (E/A, P=0.03 and E/E', P=0.02). Gal-3 was also associated with progressive diastolic dysfunction, and increased levels corresponded to the course of disease (P=0.012). Detailed analyses of ROC curves suggested that Gal-3 levels could discriminate patients with grade III diastolic dysfunction (area under the curve [AUC]=0.770, P=0.005). CONCLUSIONS: Gal-3 demonstrates remarkable effectiveness in the diagnosis of patients suffering from severe grade diastolic dysfunction. Increasing levels of Gal-3 possibly reflect the progressive course of HFpEF, as classified by the echocardiographic grades of diastolic dysfunction.
Classification ; Diagnosis ; Echocardiography* ; Galectin 3* ; Heart Failure ; Humans ; Prospective Studies ; ROC Curve ; Ventricular Remodeling

A variety of methods for testosterone replacement therapy (TRT) exist, and the major potential risks of TRT have been well established. The risk of developing polycythemia secondary to exogenous testosterone (T) has been reported to range from 0.4% to 40%. Implantable T pellets have been used since 1972, and secondary polycythemia has been reported to be as low as 0.4% with this administration modality. However, our experience has suggested a higher rate. We conducted an institutional review board-approved, single-institution, retrospective chart review (2009-2013) to determine the rate of secondary polycythemia in 228 men treated with subcutaneously implanted testosterone pellets. Kaplan-Meyer failure curves were used to estimate time until the development of polycythemia (hematocrit >50%). The mean number of pellets administered was 12 (range: 6-16). The mean follow-up was 566 days. The median time to development of polycythemia whereby 50% of patients developed polycythemia was 50 months. The estimated rate of polycythemia at 6 months was 10.4%, 12 months was 17.3%, and 24 months was 30.2%. We concluded that the incidence of secondary polycythemia while on T pellet therapy may be higher than previously established.
Adult ; Aged ; Androgens/adverse effects* ; Drug Implants ; Hematocrit ; Hormone Replacement Therapy/methods* ; Humans ; Hypogonadism/drug therapy* ; Kaplan-Meier Estimate ; Male ; Middle Aged ; Polycythemia/epidemiology* ; Retrospective Studies ; Testosterone/adverse effects*