Background: Anesthesia and surgery may influence toddlers' sensory processing and consequently postoperative adjustment and behavior. This is the first study to: 1) test pre- to postoperative changes in sensory processing after pediatric anesthesia using the validated Infant/Toddler-Sensory Profile for 7-36 months (ITSP7-36); 2) identify putative predictors of these changes.
Methods: This prospective cohort study included 70 healthy boys (ASA I & II), aged 18-30 months, who underwent circumcision for religious reasons. Exclusion: boys with prior surgery and known developmental delay.
Primary Outcome: changes in sensory processing from the day of admission to day 14 postoperatively. The accompanying parent completed the ITSP7-36. Putative predictors: 1) child's preoperative emotional/behavioral problems; 2) child's state anxiety at induction; 3) postoperative pain at home. All children received standardized anesthesia and pain management.
Results: For 45 boys, assessments were completed at both time points. Significant changes in sensory processing (mean ITSP7-36 scores) were found on: low registration (47.5 to 49.8; P=0.015), sensory sensitivity (45.2 to 48.0; P=0.011), sensation avoiding (48.2 to 51.3; P=0.010), low threshold (93.4 to 99.4; P=0.007), auditory processing (39.3 to 43.3; P=0.000) and tactile processing (53.9 to 58.4; P=0.002). Higher scores on emotional/behavioral problems predicted changes on sensory processing.
Conclusions: Sensory processing of these toddlers had changed after anesthesia. Children with more pre-existent emotional/behavioral problems are more vulnerable to these changes.
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http://dx.doi.org/10.23736/S0375-9393.18.12132-8 | DOI Listing |
PLoS One
January 2025
Monash University, Melbourne, Australia.
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Portugal Football School, Portuguese Football Federation, Oeiras, Portugal.
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January 2025
Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
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School of Economics & Management, Beijing Information Science & Technology University, Beijing, China.
E-commerce faces challenges such as content homogenization and high perceived risk among users. This paper aims to predict perceived risk in different contexts by analyzing review content and website information. Based on a dataset containing 262,752 online reviews, we employ the KeyBERT-TextCNN model to extract thematic features from the review content.
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