Profile of brief symptom inventory-18 (BSI-18) scores in collegiate athletes: A CARE Consortium study

Objective: The goal of this study was to characterize normative scores for the Brief Symptom Inventory (BSI-18) in collegiate athletes to inform decision making about the need for psychological health services in this group. Methods: Collegiate student-athletes (N = 20,034) from 25 universities completed the BSI-18 at their preseason baseline assessment. A subgroup (n = 5,387) underwent multiple baseline assessments. Global Severity Index (GSI) scores were compared to community norms and across multiple timepoints. Results: Collegiate athletes reported significantly lower GSI scores than published community norms (ppn = 230; 1.15%). Conclusions: For collegiate student-athletes, published BSI-18 threshold scores identify only extreme outliers who might benefit from additional behavioral health evaluation. Alternatively, use of threshold scores ≥ the 90th percentile identifies a more realistic 11.4% of the population, with higher likelihood of prior concussion and/or psychiatric disorders.</p

Estimation of the limits of quantification (LOQ), based on the “Derivative” (A), the “Interval” (B), and the “Coefficient of Variation” methods (C).

<p>In A solid and dashed black lines show the standard curve and the second order derivative, respectively. Dashed blue lines show the limits of quantification. In B, solid and dashed red lines show the asymptote coefficients and the 95% confidence interval, respectively. Dashed blue lines show the LOQ, and dashed black lines show the 95% prediction interval of the standard curve. In C, solid red and black lines show the coefficient of variation (CV) estimated for each fitted concentration and the standard curve, respectively. Dashed blue lines show the LOQ, and the dashed black line shows the user specified CV cutoff.</p

Cartoon showing the influence of the Subtract method into the standard samples.

<p>Background noise calculated as the geometric mean of the blank controls is shown as a horizontal dashed line. (A) Standard curve plotting the MFI in the <u><b>original scale</b></u> as a function of concentration; (B) Standard curve plotting the MFI in the <u><b>original scale</b></u> as a function of concentration <u><b>after subtracting</b></u> the MFI of the blank control from all standard samples; (C) Standard curve plotting the MFI in the <u><b>log10 scale</b></u> as a function of concentration; and (D) Standard curve plotting the MFI in the <u><b>log10 scale</b></u> as a function of concentration <u><b>after subtracting</b></u> the MFI of the blank control from all standard samples.</p

Ranked log₁₀MAD of log₁₀MFI for the 16 combinations of the four assay conditions in the positive control.

<p>The x-axis shows the mean log₁₀MAD of log₁₀MFI and 95% confidence intervals. A) By antigen and B) Combining all antigens. The y-axis shows combinations of the following conditions, ordered by the value of the mean log₁₀MAD: temperature of sample-bead incubation (37 = 37°C and 22 = 22°C), sample predilution (D = daily and S = stock), beads coupling (S = single and C = three or more combined), and plate washing (A = automatic and M = manual).</p

Format of the input dataset used by the package.

<p>(A) Dataset user can create for the package; (B) package automated data management: datasets produced by the package when data is automatically extracted from the assay machine.</p

Standardized residuals as a function of the predicted base 10 logarithm median fluorescence intensity (MFI) for the four emulated datasets based on standard curves fitted using the four different approaches to treat background noise.

<p>Points outside of the range specified by the dashed lines are in red and labeled according to the well location in the 96-well plate. (A) Standard curve fitted using “Ignore” background noise method; (B) standard curve fitted including the background noise as an extra point when fitting the curve, using “Include” background method; (C) standard curve fitted subtracting the background noise using the “Subtract” method; (D) standard curve fitted constraining the lower asymptote to the background noise using the “Constraint” method.</p

Schematic representation of the functions and arguments included in the drLumi package.

<p>The names of the functions are shown inside blue boxes, options and names of arguments of the functions are shown inside red ellipses. The green boxes show the starting points of the flow depending on the origin of the raw data.</p

Standard curves based on the four-parameter log-logistic model (FCT) fitted using the four alternative approaches to treat background noise (BKG) in the four emulated datasets.

<p>The dashed lines represent the confidence interval of the curve, and the green line represents the geometric mean of the blank controls. RS = R-square. (A) Standard curve fitted using “Ignore” background noise method; (B) standard curve fitted including the background noise as an extra point when fitting the curve, using “Include” background method; (C) standard curve fitted subtracting the background noise using the “Subtract” method; (D) standard curve fitted constraining the lower asymptote to the background noise using the “Constraint” method.</p

Comparison of automatically generated results provided by the package before/after flagging outliers for the Subtract background method for the simulated analytes.

<p>In Analyte 1 no outliers and no missing values were included, in Analyte 2 seven missing values were included, in Analyte 3 one outlier and seven missing values were included and in Analyte 4 two outliers were included in the original simulated data. Only in Analyte 4 outliers were flagged by the package and their removal changed results. For Analyte 4, the limits of quantification for the coefficient of variation method could not be estimated with all data due to the fact that the minimum coefficient of variation value was larger than the specified 30% cutoff.</p

Standard curves based on the four-parameter log-logistic model (FCT) fitted using the four alternative approaches to treat background noise (BKG) in the four emulated datasets after the outliers identified in Fig 5 were flagged (empty circles).

<p>The dashed lines represent the confidence interval of the curve, and the green line represents the geometric mean of the blank controls. RS = R-square. (A) Standard curve fitted using “Ignore” background noise method; (B) standard curve fitted including the background noise as an extra point when fitting the curve, using “Include” background method; (C) standard curve fitted subtracting the background noise using the “Subtract” method; (D) standard curve fitted constraining the lower asymptote to the background noise using the “Constraint” method.</p