Birth weight under 2500 g is a significant risk factor for IH repairs. Other risk factors are male gender, past history of lung diseases, and ventilator supports.

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Background: To evaluate the occurrence of constipation after anorectal malformations (ARM) repair and the results of laxative treatment. Methods: Between August 2012 and July 2017, the clinical data of patients with ARMs was prospectively collected. The patients were divided into two groups, good types and poor types. Good types included rectoperineal, rectovestibular, rectourethral bulbar, and no fistula. Risk factors were defined as spinal cord anomalies, sacral ratio <0.4, or cognitive impairment. Success was defined as that laxative could be tapered. Results: Eighty-four patients were enrolled with mean age of 6.3 AE 7.8 (0.6e59.9) years. The mean age of onset of constipation was 12.8 AE 8.3 months and the mean interval was 5.9 AE 5.1 months after reconstructions. The interval was not significantly different between patients with good types and poor types. In 23 patients with severe constipation being treated for >6 months, 14 of 18 (77.8%) patients with good types were classified as success, whereas only 1 of 5 (20%) patients with poor types was (p Z 0.02). In patients with good types, 9 of 9 (100%) patients with no risk factors were successful; however, only 5 out of 9 (55.6%) patients with risk factors were successful (p Z 0.02). Conclusion: Constipation occurs shortly after operations. Patients with good types and no risk factors are susceptible to weaning laxatives.

An automation system that can execute natural language instructions by driving the user interface (UI) of an application can benefit users, especially when situationally or permanently impaired. Traditional automation systems (manual scripting, programming by demonstration tools, etc.) do not produce generalizable models that can tolerate changes in the UI or task workflow. Machine-learned automation agents generalize better, but either work only in simple, handcrafted applications or rely on large pre-trained models, which may be too computationally expensive to run on mobile devices. In this paper, we propose UINav, a demonstration-based agent maker system. UINav agents are lightweight enough to run on mobile devices, yet they achieve high success rates with a modest number of task demonstrations. To minimize the number of task demonstrations, UINav includes a referee model that allows users to receive immediate feedback on tasks where the agent is failing to best guide efforts to collect additional demonstrations. Further, UINav adopts macro actions to reduce an agent's state space, and augments human demonstrations to increase the diversity of training data. Our evaluation demonstrates that with an average of 10 demonstrations per task UINav can achieve an accuracy of 70% or higher, and that with enough demonstrations it can achieve near-perfect success rates on 40+ different tasks.