Categorical and Dimensional Definitions and Evaluations of Symptoms of ADHD: History of the SNAP and the SWAN Rating Scales.
Bibliographic record
Abstract
An earlier version of this article was originally submitted for publication in early 2000 to introduce a new dimensional of concept of Attention Deficit Hyperactivity Disorder (ADHD) provided by the Strengths and Weaknesses of ADHD-symptoms and Normal-behavior (SWAN) rating scale. The SWAN was developed to correct some obvious deficiencies of the Swanson, Nolan and Pelham (SNAP) rating scale that was based on the categorical concept of ADHD. The first submission was not accepted for publication, so a draft of the article was posted on a website (www.ADHD.net). The SWAN scale was published as a table in a review article (Swanson et al, 2001) to make it available to those interested in this dimensional approach to assessment of ADHD. Despite its relative inaccessibility, the SWAN has been used in several genetic studies of ADHD (e.g., Hay, Bennett, Levy, Sergeant, & Swanson, 2005; Cornish et al, 2005) and has been translated into several languages for European studies of ADHD (e.g., Lubke et al, 2006; Polderman et al, 2010) and into Spanish for studies in the United States (e.g., Lakes, Swanson, & Riggs, 2011; Kudo et al., this issue). Recently, invitations to include the SWAN in the PhenX Toolkit (www.phenx.org) for genomic studies (Hamilton et al, 2011) and to describe thedimensional approach of the SWAN for discussion of diagnostic (Swanson, Wigal, & Lakes, 2009) and ethical (Swanson, Wigal, Lakes, &Volkow, 2011) issues has convinced us that the unpublished article is still relevant after more than a decade, so it is presented here with some minor updates. We use examples (a) to document some consequences (e.g., over-identification of extreme cases) of using statistical cutoffs based on the assumption for a distribution of SNAP ratings that is highly skewed and (b) to show how the SWAN corrects the skewness of the SNAP by rewording the items on the scale and using a wider range of rating alternatives, which corrects the tendency to over-identify extreme cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".