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All exercises including the novel clip-applying model demonstrated good acceptability and evidence of construct validity (face, content, concurrent and convergent validity) for assessment of basic laparoscopic skill for urologic surgeons.

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Deep neural networks have shown promising results for various clinical prediction tasks such as diagnosis, mortality prediction, predicting duration of stay in hospital, etc. However, training deep networks such as those based on Recurrent Neural Networks (RNNs) requires large labeled data, significant hyper-parameter tuning effort and expertise, and high computational resources. In this work, we investigate as to what extent can transfer learning address these issues when using deep RNNs to model multivariate clinical time series. We consider two scenarios for transfer learning using RNNs: i) domain-adaptation, i.e., leveraging a deep RNN -namely, TimeNet -pre-trained for feature extraction on time series from diverse domains, and adapting it for feature extraction and subsequent target tasks in healthcare domain, ii) task-adaptation, i.e., pre-training a deep RNN -namely, HealthNet -on diverse tasks in healthcare domain, and adapting it to new target tasks in the same domain. We evaluate the above approaches on publicly available MIMIC-III benchmark dataset, and demonstrate that (a) computationally-efficient linear models trained using features extracted via pre-trained RNNs outperform or, in the worst case, perform as well as deep RNNs and statistical hand-crafted features based models trained specifically for target task; (b) models obtained by adapting pre-trained models for target tasks are significantly more robust to the size of labeled data compared to task-specific RNNs, while also being computationally efficient. We, therefore, conclude that pre-trained deep models like TimeNet and HealthNet allow leveraging the advantages of deep learning for clinical time series analysis tasks, while also minimize dependence on hand-crafted features, deal robustly with scarce labeled training data scenarios without overfitting, as well as reduce dependence on expertise and resources required to train deep networks from scratch (e.g. neural network architecture selection and hyper-parameter tuning efforts).

BackgroundThe International Academy of Cytology (IAC) Yokohama system is a recently proposed system for reporting breast cytology by fine needle aspiration biopsies (FNAB). Multiple studies are needed to confirm the risk of malignancy (ROM) of the various reporting categories of this system. The present article studies the accuracy of the IAC Yokohama system in our center.MethodsOver a period of 1 year (September 2018‐August 2019), all cases of breast masses assessed by FNAB and histological correlation were studied retrospectively. Sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) and overall accuracy of the IAC Yokohama system for diagnosing malignancy were assessed. The rates of malignancy (ROM) of each diagnostic category were also estimated.ResultsThree hundred and twenty‐one FNABs had cyto‐histological correlation. The percent sensitivity (with 95% Confidence Intervals) when the atypical, suspicious of malignancy and the malignant categories were regarded as positive for malignancy were 98.2% [95.5%, 99.5%], 96.0% [92.5%, 98.2%], and 86.7% [81.5%, 90.8%] respectively. The percent specificity (with 95% Confidence intervals) for the same categories in the same order were 59.5% [47.4%, 70.7%], 91.9% [83.2%, 97.0%], and 100% [95.1%, 100%] respectively. The area under curve (AUC) for diagnosing malignancy was 0.981[0.963, 0.993]. The ROM for the benign, atypical, suspicious of malignancy and malignant category were 8.3% [2.3%, 20.0%], 17.2% [5.8%, 35.8%], 77.8% [57.7%, 91.4%], and 100% [98.1%, 100%] respectively.ConclusionThe IAC Yokohama system is suitable for accurately reporting breast lesions on FNAB.

The present clinical series of Wolfram syndrome reveals a varied clinical presentation of the syndrome and some new associations.

Introduction:FESS (Functional Endoscopic Sinus Surgery) is done via endoscope and the area is highly vascular thus it becomes important to minimize bleeding. Hence we require hypotensive anesthesia. Various drugs have been used for the purpose: nitroglycerine (NTG), sodium nitroprusside(SNP),propofol,beta blockers, calcium channel blockers, higher concentrations of inhalational anesthetics etc. Since all these drugs have certain limitations there was a search for safer and more effective drug .Dexmedetomidine, a newer alphaadrenoceptor agonist,fulfills this requirement since it is short acting, has no residual effects, produces sedation and analgesia and reduces mean arterial pressure thereby reducing intraoperative blood loss. Aims: To study the efficacy of dexmedetomidine in providing hypotensive anaesthesia so as to minimize bleeding during endoscopic sinus surgery in a randomized double blind control study. Method:40 patients of ASA grade I/II, aged18-55yrs,scheduled for endoscopic sinus surgery, were randomly allocated into two groups of twenty each by sealed envelope method. Group C: received Normal saline as loading dose of 1mcg/kg intravenously followed by maintenance infusion of 0.6mcg/kg intravenously, Group D: received dexmedetomidine in similar doses. The loading dose was given slowly over 15 minutes. The surgery was started after the loading dose was over. Both the anaesthetist conducting the study and the patient were blinded to the study drug. The amount of blood loss, doses of intravenous& inhalation agents and side effects were studied. Results:The patient characteristics did not differ in bothcontrol(C) and study (D) groups (Table1). Blood losses were lower in group D as compared to group C (p = 0.03).Total dose of fentanyl (p< 0.001),propofol(p <0.001)and isoflurane (p =0.04)were lower in group D as compared to group C. The VAS scores in the immediate postoperative periods were also lower in the group D (p =0.03).The only side effect noted was bradycardia (2patients)which however reverted with discontinuation of the drug.Atropine was not required. Conclusions:Dexmedetomidine is a safe &effective adjuvant for hypotensive anaesthesia to decrease bleeding and thus provide bloodless field during FESS surgery. It also decreases the dose requirements of propofol,fentanyl and isoflurane. It is also an effective analgesic agent.