OBJECTIVES: Validated prognostic tools for pediatric community-acquired pneumonia (CAP) do not exist. Thus, clinicians rely on "gestalt" in management decisions for children with CAP. We sought to determine the ability of clinician gestalt to predict severe outcomes. METHODS:We performed a prospective cohort study of children 3 months to 18 years old presenting to a pediatric emergency department (ED) with lower respiratory infection and receiving a chest radiograph for suspected CAP from 2013 to 2017. Clinicians reported the probability that the patient would develop severe complications of CAP (defined as respiratory failure, empyema or effusion, lung abscess or necrosis, metastatic infection, sepsis or septic shock, or death). The primary outcome was development of severe complications.RESULTS: Of 634 children, 37 (5.8%) developed severe complications. Of children developing severe complications after the ED visit, 62.1% were predicted as having ,10% risk by the ED clinician. Sensitivity was .90% at the ,1% predicted risk threshold, whereas specificity was .90% at the 10% risk threshold. Gestalt performance was poor in the low-intermediate predicted risk category (1%-10%). Clinicians had only fair ability to discriminate children developing complications from those who did not (area under the receiver operator characteristic curve 0.747), with worse performance from less experienced clinicians (area under the receiver operator characteristic curve 0.693). CONCLUSIONS:Clinicians have only fair ability to discriminate children with CAP who develop severe complications from those who do not. Clinician gestalt performs best at very low or higher predicted risk thresholds, yet many children fall in the low-moderate predicted risk range in which clinician gestalt is limited. Evidence-based prognostic tools likely can improve on clinician gestalt, particularly when risk is low-moderate.WHAT'S KNOWN ON THIS SUBJECT: Validated prognostic tools do not exist for children with community-acquired pneumonia, leaving clinicians to rely on gestalt for decisionmaking. Data on the predictive ability of clinician gestalt for clinical outcomes in children with respiratory infections are limited.WHAT THIS STUDY ADDS: Clinicians have fair ability to discriminate children with community-acquired pneumonia who develop severe complications. Clinician gestalt performs best at very low or higher predicted risk, yet many children fall in the low-intermediate predicted risk range in which clinician gestalt is more limited.

Purpose To predict cycloplegic refractive error using measurements obtained under noncycloplegic conditions. Method Refractive error was measured in 5- to 18-year-old Chinese students using a NIDEK autorefractor before and after administration of 0.5% tropicamide. Spherical equivalent (SER) in diopters (D) was calculated as sphere plus half cylinder. A multivariable prediction model for cycloplegic SER was developed using data from students in Jinyun ( n = 1938) and was validated using data from students in Hangzhou ( n = 1498). The performance of the prediction model was evaluated using R 2 , mean difference between predicted and measured cycloplegic SER, and sensitivity and specificity for predicting myopia (cycloplegic SER ≤ −0.5 D). Results Among 3436 students (mean age, 9.7 years; 51% female), the mean (SD) noncycloplegic and cycloplegic SER values were −1.12 (1.97) D and −0.20 (2.19) D, respectively. The prediction model that included demographics, noncycloplegic SER, axial length/corneal curvature radius ratio, uncorrected visual acuity (UCVA), and intraocular pressure predicted cycloplegic SER with R 2 of 0.93 in the development dataset and 0.92 in the validation dataset. The mean (SD) differences between predicted and measured cycloplegic SER were 0.0 (0.55) D in the development dataset and 0.06 (0.64) D in the validation dataset. In both the development and validation datasets, the combination of predicted SER and UCVA yielded high sensitivity (91.4% and 91.9%, respectively) and specificity (95.0% and 90.1%, respectively) for detecting myopia. Conclusions Cycloplegic refractive error can be predicted using measurements obtained under noncycloplegic conditions. The prediction model could potentially be used to correct the myopia prevalence in epidemiological studies in which administering cycloplegic agent on all participants is not feasible. Translational Relevance The prediction model may provide a tool for correcting the overestimation of myopia from noncycloplegic refractive error in future epidemiological studies in which administering cycloplegic agent on all participants is not feasible.

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