Star CBM
Expressive Nonsense Words

Descriptive Information

Please provide a description of your tool:

In Expressive Nonsense Words assessments, students pronounce three-letter consonant-vowel-consonant (CVC) nonsense words based on the sounds the letters make. (Short vowel sounds are expected.) This is an early measure of the student’s decoding skill.

The tool is intended for use with the following grade(s).

Preschool / Pre - kindergarten
Kindergarten
First grade
Second grade
Third grade
Fourth grade
Fifth grade
Sixth grade
Seventh grade
Eighth grade
Ninth grade
Tenth grade
Eleventh grade
Twelfth grade

The tool is intended for use with the following age(s).

0-4 years old
5 years old
6 years old
7 years old
8 years old
9 years old
10 years old
11 years old
12 years old
13 years old
14 years old
15 years old
16 years old
17 years old
18 years old

The tool is intended for use with the following student populations.

Students in general education
Students with disabilities
English language learners

ACADEMIC ONLY: What skills does the tool screen?

Reading

Phonological processing:

RAN
Memory
Awareness
Letter sound correspondence
Phonics
Structural analysis

Word ID

Accuracy
Speed

Nonword

Accuracy
Speed

Spelling

Accuracy
Speed

Passage

Accuracy
Speed

Reading comprehension:

Multiple choice questions
Cloze
Constructed Response
Retell
Maze
Sentence verification
Other (please describe):

Listening comprehension:

Multiple choice questions
Cloze
Constructed Response
Retell
Maze
Sentence verification
Vocabulary
Expressive
Receptive

Mathematics

Global Indicator of Math Competence

Accuracy
Speed
Multiple Choice
Constructed Response

Early Numeracy

Accuracy
Speed
Multiple Choice
Constructed Response

Mathematics Concepts

Accuracy
Speed
Multiple Choice
Constructed Response

Mathematics Computation

Accuracy
Speed
Multiple Choice
Constructed Response

Mathematic Application

Accuracy
Speed
Multiple Choice
Constructed Response

Fractions/Decimals

Accuracy
Speed
Multiple Choice
Constructed Response

Algebra

Accuracy
Speed
Multiple Choice
Constructed Response

Geometry

Accuracy
Speed
Multiple Choice
Constructed Response

Other (please describe):

Please describe specific domain, skills or subtests:

BEHAVIOR ONLY: Which category of behaviors does your tool target?

Internalizing
Externalizing
Internalizing and Externalizing

BEHAVIOR ONLY: Please identify which broad domain(s)/construct(s) are measured by your tool and define each sub-domain or sub-construct.

Acquisition and Cost Information

Where to obtain:

Email Address

answers@renaissance.com

Address

Renaissance Learning, PO Box 8036, Wisconsin Rapids, WI 54495

Phone Number

(800) 338-4204

Website

https://www.renaissance.com

Initial cost for implementing program:

Cost

Unit of cost

Replacement cost per unit for subsequent use:

Cost

Unit of cost

Duration of license

Additional cost information:

Describe basic pricing plan and structure of the tool. Provide information on what is included in the published tool, as well as what is not included but required for implementation.

Total cost will depend on the number of schools and students; annual subscription required. Please contact: answers@renaissance.com or (800) 338-4204 for specific details on pricing. Star CBM is cloud-based and purchase includes the application, technical manual, administration instructions, and quick-start guidance for professional learning.

Provide information about special accommodations for students with disabilities.

Star CBM offers braille support via downloadable .BRF files for most reading and math assessment measures. For reading assessments, uncontracted braille files are available for all assessments; for Passage Oral Reading, contracted braille files are also available if you prefer to use those. For math assessments (including Rapid Number Naming), both UEB and Nemeth braille files are available. Use the Accommodations preference to indicate which students should have braille files made available when you generate Star CBM assessments. The assigned .BRF braille file will download and can be sent to your braille embosser. Any other Star CBM accommodations should be consistent with requirements for individual students you are assessing. In general and when appropriate, to use Star CBM as designed, we recommend not varying either the content of individual forms/measures or the time limit for completing each measure (1 minute). In addition to braille support, changes in font size, highlighting, contrast, or other changes that do not vary the content and timing are possible and, based on student need, appropriate.

Administration

BEHAVIOR ONLY: What type of administrator is your tool designed for?

General education teacher
Special education teacher
Parent
Child
External observer
Other

If other, please specify:

What is the administration setting?

Direct observation
Rating scale
Checklist
Performance measure
Questionnaire
Direct: Computerized
One-to-one
Other

If other, please specify:

Does the tool require technology?

Yes

If yes, what technology is required to implement your tool? (Select all that apply)

Computer or tablet
Internet connection
Other technology (please specify)

If your program requires additional technology not listed above, please describe the required technology and the extent to which it is combined with teacher small-group instruction/intervention:

What is the administration context?

Individual
Small group   If small group, n=
Large group   If large group, n=
Computer-administered
Other

If other, please specify:

What is the administration time?

Time in minutes

1

per (student/group/other unit)

student

Additional scoring time:

Time in minutes

0

per (student/group/other unit)

student

ACADEMIC ONLY: What are the discontinue rules?

No discontinue rules provided
Basals
Ceilings
Other

If other, please specify:

Are norms available?

Yes

Are benchmarks available?

Yes

If yes, how many benchmarks per year?

Benchmarks are suggested for each measure, specifically for each grade and season.

If yes, for which months are benchmarks available?

Fall, Winter, Spring

BEHAVIOR ONLY: Can students be rated concurrently by one administrator?

If yes, how many students can be rated concurrently?

Training & Scoring

Training

Is training for the administrator required?

Yes

Describe the time required for administrator training, if applicable:

Less than one hour of training

Please describe the minimum qualifications an administrator must possess.

No minimum qualifications

Are training manuals and materials available?

Yes

Are training manuals/materials field-tested?

Yes

Are training manuals/materials included in cost of tools?

Yes

If No, please describe training costs:

Can users obtain ongoing professional and technical support?

Yes

If Yes, please describe how users can obtain support:

Renaissance Technical Support Staff

Scoring

How are scores calculated?

Manually (by hand)
Automatically (computer-scored)
Other

If other, please specify:

Do you provide basis for calculating performance level scores?

Yes

What is the basis for calculating performance level and percentile scores?

Age norms
Grade norms
Classwide norms
Schoolwide norms
Stanines
Normal curve equivalents

What types of performance level scores are available?

Raw score
Standard score
Percentile score
Grade equivalents
IRT-based score
Age equivalents
Stanines
Normal curve equivalents
Developmental benchmarks
Developmental cut points
Equated
Probability
Lexile score
Error analysis
Composite scores
Subscale/subtest scores
Other

If other, please specify:

Does your tool include decision rules?

No

If yes, please describe.

Can you provide evidence in support of multiple decision rules?

No

If yes, please describe.

Please describe the scoring structure. Provide relevant details such as the scoring format, the number of items overall, the number of items per subscale, what the cluster/composite score comprises, and how raw scores are calculated.

For each student, you will see the student's grade and the student's latest Correct Per Minute (CPM) score (if any) for each type of assessment. When benchmarks are available for a student's score and the current season (Fall, Winter, or Spring), the background color shows which benchmark category the score falls into.

Describe the tool’s approach to screening, samples (if applicable), and/or test format, including steps taken to ensure that it is appropriate for use with culturally and linguistically diverse populations and students with disabilities.

For Expressive Nonsense Words assessments, students pronounce three-letter consonant-vowel-consonant (CVC) nonsense words based on the sounds the letters make. (Short vowel sounds are expected.) This is an early measure of the student’s decoding skill; they can be used to identify candidates for early intervention. Both criterion- and norm-referenced scores are automatically reported to inform instructional decisions. Reports and dashboards guide educators through screening and related decisions. Default risk categories, based on national norms, are provided, although they can be adjusted by local school leaders to best fit local populations. During test content development, every effort is made to avoid the use of stereotypes, potentially offensive language or characterizations, and descriptions of people or events that could be construed as being offensive, demeaning, patronizing, or otherwise insensitive. Field testing involved a diverse population. Additionally, bias analyses are performed for Gender, Ethnicity, Special Education Status, and English Language Learner Status.

Classification Accuracy & Cross-Validation Summary

Grade	Grade 1	Grade 2
Classification Accuracy Fall
Classification Accuracy Winter
Classification Accuracy Spring

DIBELS Nonsense Word Fluency- Whole Recoded Correctly

Classification Accuracy

Select time of year

Fall

Winter

Spring

Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.

DIBELS Nonsense Word Fluency (NWF) is a brief, direct measure of the alphabetic principle and basic phonics. It assesses knowledge of basic letter-sound correspondences and the ability to blend letter sounds into consonant-vowel-consonant (CVC) and vowel-consonant (VC) words. The test items used for NWF are phonetically regular make-believe (nonsense or pseudo) words. To successfully complete the NWF task, students must rely on their knowledge of letter-sound correspondences and how to blend sounds into whole words. Whole Words Read (WWR) is the number of make-believe words read correctly as a whole word, one time and only one time, without first being sounded out. For example, if the student reads dif as “dif,” the score is 3 points for CLS and 1 point for WWR, but if the student reads dif as “/d/ /i/ /f/ dif,” the score is 3 points for CLS but 0 points for WWR.

Do the classification accuracy analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

Describe how the classification analyses were performed and cut-points determined. Describe how the cut points align with students at-risk. Please indicate which groups were contrasted in your analyses (e.g., low risk students versus high risk students, low risk students versus moderate risk students).

Using the SAS program ROC analyses were conducted on the sample. The DIBELS NWF was administered in the Winter of grade 1. DIBELS was used to determine students “at-risk” and in “intensive need” and was administered three months later in the Winter and Spring (as specified in the NCII criteria). The 20th percentile on the NWF was used to determine the cut-points on the DIBELS. The results of the ROC analysis provided the Area Under the Curve (AUC) with 95% confidence intervals for determining the lower and upper bounds. In addition, the ROC analysis provides the Coordinates of the Curve provide Sensitivity and Specificity estimates. In addition, the coordinates are used to determine “risk” cut-points on NWF that are associated with different levels of sensitivity and specificity. The cut-points on the NWF were then used for further classification analysis in determining overall correct classification rates, false positives, false negative, Positive Predictive Power, and Negative Predictive Power.

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

No

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

Cross-Validation

Has a cross-validation study been conducted?

No

If yes,

Select time of year.

Fall

Winter

Spring

Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.

Do the cross-validation analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

Describe how the cross-validation analyses were performed and cut-points determined. Describe how the cut points align with students at-risk. Please indicate which groups were contrasted in your analyses (e.g., low risk students versus high risk students, low risk students versus moderate risk students).

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

---

DIBELS Oral Reading Fluency- Words Read Correctly

Classification Accuracy

Select time of year

Fall

Winter

Spring

Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.

DIBELS Oral Reading Fluency (ORF) is a measure of advanced phonics and word attack skills, accurate and fluent reading of connected text, and reading comprehension. The ORF passages and procedures are based on the program of research and development of Curriculum-Based Measurement of reading by Stan Deno and colleagues at the University of Minnesota (Deno, 1989). There are two parts to ORF: orally reading a passage and retelling the passage. For the oral reading part, students are given an unfamiliar, grade-level passage of text and asked to read for 1 minute. Errors such as substitutions, omissions, and hesitations for more than 3 seconds are marked while listening to the student read aloud. For benchmark assessment, students are asked to read three different grade-level passages for 1 minute each. The score is the median number of words read correctly and the median number of errors across the three passages. Using the median score from three passages gives the best indicator of student performance over a range of different text and content. The oral reading part of the measure can be used from the middle of first grade through the end of sixth grade.

Do the classification accuracy analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

Describe how the classification analyses were performed and cut-points determined. Describe how the cut points align with students at-risk. Please indicate which groups were contrasted in your analyses (e.g., low risk students versus high risk students, low risk students versus moderate risk students).

Using the SAS program ROC analyses were conducted on the sample. The DIBELS ORF was administered in the Winter of grade 2. DIBELS was used to determine students “at-risk” and in “intensive need” and was administered three months later in the Winter and Spring (as specified in the NCII criteria). The 20th percentile on the ORF was used to determine the cut-points on the DIBELS. The results of the ROC analysis provided the Area Under the Curve (AUC) with 95% confidence intervals for determining the lower and upper bounds. In addition, the ROC analysis provides the Coordinates of the Curve provide Sensitivity and Specificity estimates. In addition, the coordinates are used to determine “risk” cut-points on ORF that are associated with different levels of sensitivity and specificity. The cut-points on the ORF were then used for further classification analysis in determining overall correct classification rates, false positives, false negative, Positive Predictive Power, and Negative Predictive Power.

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

No

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

Cross-Validation

Has a cross-validation study been conducted?

No

If yes,

Select time of year.

Fall

Winter

Spring

Describe the criterion (outcome) measure(s) including the degree to which it/they is/are independent from the screening measure.

Do the cross-validation analyses examine concurrent and/or predictive classification?

Concurrent
Predictive

Describe when screening and criterion measures were administered and provide a justification for why the method(s) you chose (concurrent and/or predictive) is/are appropriate for your tool.

Describe how the cross-validation analyses were performed and cut-points determined. Describe how the cut points align with students at-risk. Please indicate which groups were contrasted in your analyses (e.g., low risk students versus high risk students, low risk students versus moderate risk students).

Were the children in the study/studies involved in an intervention in addition to typical classroom instruction between the screening measure and outcome assessment?

If yes, please describe the intervention, what children received the intervention, and how they were chosen.

---

Classification Accuracy - Fall

Evidence	Grade 1	Grade 2
Criterion measure	DIBELS Nonsense Word Fluency- Whole Recoded Correctly	DIBELS Oral Reading Fluency- Words Read Correctly
Cut Points - Percentile rank on criterion measure	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	9 CPM	16 CPM
Classification Data - True Positive (a)	4	12
Classification Data - False Positive (b)	4	7
Classification Data - False Negative (c)	10	9
Classification Data - True Negative (d)	58	60
Area Under the Curve (AUC)	0.80	0.91
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.68	0.85
AUC Estimate’s 95% Confidence Interval: Upper Bound	0.91	0.97

Statistics	Grade 1	Grade 2
Base Rate	0.18	0.24
Overall Classification Rate	0.82	0.82
Sensitivity	0.29	0.57
Specificity	0.94	0.90
False Positive Rate	0.06	0.10
False Negative Rate	0.71	0.43
Positive Predictive Power	0.50	0.63
Negative Predictive Power	0.85	0.87

Sample	Grade 1	Grade 2
Date	Fall 2019	Fall 2019
Sample Size	76	88
Geographic Representation
Male
Female
Other
Gender Unknown
White, Non-Hispanic
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability
English Language Learner

Classification Accuracy - Winter

Evidence	Grade 1	Grade 2
Criterion measure	DIBELS Nonsense Word Fluency- Whole Recoded Correctly	DIBELS Oral Reading Fluency- Words Read Correctly
Cut Points - Percentile rank on criterion measure	20	20
Cut Points - Performance score on criterion measure
Cut Points - Corresponding performance score (numeric) on screener measure	12 CPM	18 CPM
Classification Data - True Positive (a)	4	9
Classification Data - False Positive (b)	16	6
Classification Data - False Negative (c)	0	2
Classification Data - True Negative (d)	91	56
Area Under the Curve (AUC)	0.96	0.95
AUC Estimate’s 95% Confidence Interval: Lower Bound	0.92	0.90
AUC Estimate’s 95% Confidence Interval: Upper Bound	1.00	1.00

Statistics	Grade 1	Grade 2
Base Rate	0.04	0.15
Overall Classification Rate	0.86	0.89
Sensitivity	1.00	0.82
Specificity	0.85	0.90
False Positive Rate	0.15	0.10
False Negative Rate	0.00	0.18
Positive Predictive Power	0.20	0.60
Negative Predictive Power	1.00	0.97

Sample	Grade 1	Grade 2
Date	Winter 2019	Winter 2019
Sample Size	111	73
Geographic Representation
Male
Female
Other
Gender Unknown
White, Non-Hispanic
Black, Non-Hispanic
Hispanic
Asian/Pacific Islander
American Indian/Alaska Native
Other
Race / Ethnicity Unknown
Low SES
IEP or diagnosed disability
English Language Learner

Reliability

Grade	Grade 1	Grade 2
Rating

*Offer a justification for each type of reliability reported, given the type and purpose of the tool.

The Star CBM Reading measures are analyzed within a classical test theory framework with scores reported in an equated correct per minute metric. CBMs are unique from other tests in that tests are typically stopped after a set amount of time (usually 1 minute of time), and most students are not expected to complete all of the items on a form within the time limit. As a result, reliability estimates that rely on total scores and that can be formulated within a classical test theory framework are appropriate for CBMs. Star CBM reports three different reliability coefficients, model based G-coefficients from generalizability theory analyses, alternate forms coefficients, and test-retest coefficients.

*Describe the sample(s), including size and characteristics, for each reliability analysis conducted.

Each reliability coefficient is computed using a different sample of students that were drawn from a field test study that took place in the fall and winter of 2019-2020. Data were collected from schools in 26 different states. For the G-coefficients based on generalizability theory, the estimate was based on a total of sample of 3440 in Grade 1 and 3146 in Grade 2 student test records where students were required to take two distinct CBM forms in the fall and two distinct CBM forms in the winter on the same day. For the alternate forms coefficients, the estimate was based on a total sample of 1,720 Grade 1 and 1,573 Grade 2 matched student test records where students took two distinct CBM forms in the fall or two distinct CBM forms in the winter on the same day. For the test-retest coefficients, the estimate was based on a total sample of 1,395 Grade 1 and 1,219 Grade 2 matched student test records students took a CBM form in the winter and took that same CBM form two to three weeks later.

*Describe the analysis procedures for each reported type of reliability.

G-coefficients from generalizability theory were estimated using a linear mixed effects model using the lme4 package in R. The mixed effect model estimated for each Star CBM Reading measure used equated correct per minute scores as the outcome and random effects for form, person, season, form order, the form by form order interaction, and the number of days since start of the field test. The variance component for persons was then divided by the variance component for persons plus the variance component of the residuals to estimate the G-coefficient from the generalizability theory analysis. The alternate forms coefficients were estimated in R by taking the Pearson product moment correlation between the equated correct per minute scores across the pairs of two alternate forms that were administered to students on the same day. The test-retest coefficients were estimated in R by taking the Pearson product moment correlation between the equated correct per minute scores between the pairs of identical tests forms that were administered to students two to three weeks apart.

*In the table(s) below, report the results of the reliability analyses described above (e.g., internal consistency or inter-rater reliability coefficients).

Type of	Subgroup	Informant	Age / Grade	Test or Criterion	n	Median Coefficient	95% Confidence Interval Lower Bound	95% Confidence Interval Upper Bound

Results from other forms of reliability analysis not compatible with above table format:

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

Do you have reliability data that are disaggregated by gender, race/ethnicity, or other subgroups (e.g., English language learners, students with disabilities)?

No

If yes, fill in data for each subgroup with disaggregated reliability data.

Type of	Subgroup	Informant	Age / Grade	Test or Criterion	n	Median Coefficient	95% Confidence Interval Lower Bound	95% Confidence Interval Upper Bound

Results from other forms of reliability analysis not compatible with above table format:

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

Validity

Grade	Grade 1	Grade 2
Rating

*Describe each criterion measure used and explain why each measure is appropriate, given the type and purpose of the tool.

DIBELS Nonsense Word Fluency (NWF) is a brief, direct measure of the alphabetic principle and basic phonics. It assesses knowledge of basic letter-sound correspondences and the ability to blend letter sounds into consonant-vowel-consonant (CVC) and vowel-consonant (VC) words. The test items used for NWF are phonetically regular make-believe (nonsense or pseudo) words. To successfully complete the NWF task, students must rely on their knowledge of letter-sound correspondences and how to blend sounds into whole words.

*Describe the sample(s), including size and characteristics, for each validity analysis conducted.

For Grade 1, concurrent sample consisted of 111 students who took both Star CBM Expressive Nonsense Words and DIBELS Nonsense Word Fluency- Whole Words Read Correctly during the Winter. For Grade 1, the predictive sample of 110 students who took both Star CBM Expressive Nonsense Words during the Fall and DIBELS Nonsense Word Fluency- Whole Words Read Correctly during the Winter. For Grade 2, the concurrent sample consisted of 73 students who took both Star CBM Expressive Nonsense Words and DIBELS Oral Reading Fluency- Whole Words Read Correctly during the Winter. In Grade 2, the predictive sample of 74 students who took both Star CBM Expressive Nonsense Words during the Fall and DIBELS Oral Reading Fluency- Whole Words Read Correctly during the Winter.

*Describe the analysis procedures for each reported type of validity.

For both the concurrent and predictive analyses, Pearson product-moment correlation coefficients were calculated along with a 95% confidence interval around the coefficient.

*In the table below, report the results of the validity analyses described above (e.g., concurrent or predictive validity, evidence based on response processes, evidence based on internal structure, evidence based on relations to other variables, and/or evidence based on consequences of testing), and the criterion measures.

Type of	Subgroup	Informant	Age / Grade	Test or Criterion	n	Median Coefficient	95% Confidence Interval Lower Bound	95% Confidence Interval Upper Bound

Results from other forms of validity analysis not compatible with above table format:

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

Describe the degree to which the provided data support the validity of the tool.

Do you have validity data that are disaggregated by gender, race/ethnicity, or other subgroups (e.g., English language learners, students with disabilities)?

No

If yes, fill in data for each subgroup with disaggregated validity data.

Type of	Subgroup	Informant	Age / Grade	Test or Criterion	n	Median Coefficient	95% Confidence Interval Lower Bound	95% Confidence Interval Upper Bound

Results from other forms of validity analysis not compatible with above table format:

Manual cites other published reliability studies:

No

Provide citations for additional published studies.

Bias Analysis

Grade	Grade 1	Grade 2
Rating	Yes	Yes

Have you conducted additional analyses related to the extent to which your tool is or is not biased against subgroups (e.g., race/ethnicity, gender, socioeconomic status, students with disabilities, English language learners)? Examples might include Differential Item Functioning (DIF) or invariance testing in multiple-group confirmatory factor models.

Yes

If yes,

a. Describe the method used to determine the presence or absence of bias:

Classification analyses were performed using a sample of Grade 1 and Grade 2 data obtained from the spring of 2022 where students took the CBM Expressive Nonsense Words measure and Star Reading. Receiver Operating Character (ROC) curve analyses were performed to evaluate the diagnostic classification accuracy of the Expressive Nonsense Words measure for identifying students as being at risk on Star Reading. For Star Reading, the cut point for defining at risk was set at the 20th percentile. The Area Under the ROC (AUC) was computed separately by grade and subgroup with 95% confidence intervals estimated for each AUC estimate. The confidence intervals for the different subgroups in each grade were compared and evaluated to determine whether there was differential classification accuracy.

b. Describe the subgroups for which bias analyses were conducted:

Separate analyses were performed for Gender, Ethnicity, Special Education Status, and English Language Learner Status.

c. Describe the results of the bias analyses conducted, including data and interpretative statements. Include magnitude of effect (if available) if bias has been identified.

The 95% AUC confidence intervals for the subgroups compared overlapped and no statistically significant differences in classification accuracy were found for any of the subgroups in any of the grades.