scholarly journals Comment on: Cancer Pain With a Neuropathic Component: A Cross-sectional Study of Its Clinical Characteristics, Associated Psychological Distress, Treatments, and Predictors at Referral to a Cancer Pain Clinic

2018 ◽  
Vol 56 (1) ◽  
pp. e8-e9
Author(s):  
Fatemeh Khosravi Shadmani ◽  
Kamyar Mansori
BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046265
Author(s):  
Shotaro Doki ◽  
Shinichiro Sasahara ◽  
Daisuke Hori ◽  
Yuichi Oi ◽  
Tsukasa Takahashi ◽  
...  

ObjectivesPsychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists.DesignCross-sectional study.SettingWe conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists.ParticipantsAn AI model of the neural network and six psychiatrists.Primary outcomeThe accuracies of the AI model and psychiatrists for predicting psychological distress.MethodsIn total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model.ResultsThe accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy.ConclusionsA machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views.


Author(s):  
Marion J. Wessels‐Bakker ◽  
Eduard A. van de Graaf ◽  
Johanna M. Kwakkel‐van Erp ◽  
Harry G. Heijerman ◽  
Wiepke Cahn ◽  
...  

2018 ◽  
Vol 154 (1) ◽  
pp. S49
Author(s):  
Shmuel Odes ◽  
Vered Slonim-Nevo ◽  
Ruslan Sergienko ◽  
Michael Friger ◽  
Doron Schwartz ◽  
...  

Author(s):  
Vijay Pratap Singh Tomar ◽  
Sandeep Sharma ◽  
Rahul Bhardwaj ◽  
Sindhuja Singh ◽  
Virendra Kumar Pal ◽  
...  

Introduction: Pigmentary Glaucoma (PG) and Pigment Dispersion Syndrome (PDS) are two different spectrums of a single disease. Since the disease is seen in younger population and is rapidly progressive blinding disease, therefore early diagnosis and treatment will reduce the burden of the disease and improve the quality of life. Aim: To evaluate clinical characteristics of PDS and PG patients in eastern part of Uttar Pradesh. Materials and Methods: This was a two years (1st January 2018 to 31st December 2019) hospital‑based retrospective cross‑sectional study of patients who attended the glaucoma clinic. Diagnosis of PDS was made when they had normal optic disc, normal visual field {with or without increased Intra Ocular Pressure (IOP)} and at least two of the following three signs were found clinically: Krukenberg spindle, homogenous moderate‑to‑heavy (≥Spaeth 2+) Trabecular Meshwork (TM) pigmentation, and any degree of zonular and/or lenticular pigment granule dusting. Patients with PDS were diagnosed with PG, if they had two or more of the following findings: initial IOP >21 mmHg, glaucomatous optic nerve damage or glaucomatous visual field loss. Various parameters such as influence of demographics, IOP, Best‑Corrected Visual Acuity (BCVA), Central Corneal Thickness (CCT), Mean Deviation (MD), Visual Field Index (VFI %), spherical equivalent and clinical finding of anterior segment of study patients were analysed. Mean, standard deviation and percentage were calculated using GraphPad Instat version 3.0. Results: Among 40 patients, nine eyes of the six patients had myopia of ‑0.5D or greater, with mean refractive error of ‑3.55±4.72 spherical equivalent. The average baseline IOP in study patients (PDS+PG), was 30.21±11.42 mmHg. Twenty four (60%) patients, either in one or both eyes had glaucoma, secondary to PDS at the initial diagnosis. Thirty three (82.5%) patients had Krukenberg spindles. Homogeneous TM pigmentation was seen in all patients. Typical spoke‑like radial Iris Transillumination Defects (ITDs) were not observed in any of the patients except in one patient, who had isolated short slit‑like trans‑illumination defects in iris crypts. Conclusion: PDS patients with normal optic disc and visual field and raised IOP, should be started prophylactic treatment and needs to be monitored more closely. Thus, the finding of PDS in Indians should alert the ophthalmologist to look for glaucoma during the initial examination.


Sign in / Sign up

Export Citation Format

Share Document