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Record W2415489278

Categorical and Dimensional Definitions and Evaluations of Symptoms of ADHD: History of the SNAP and the SWAN Rating Scales.

2012· article· en· W2415489278 on OpenAlexaff
Sabrina Schuck, Miranda Mann Porter, Caryn L. Carlson, Catharina A. Hartman, Joseph A. Sergeant, Walter Clevenger, Michael Wasdell, Richard McCleary, Kimberley D. Lakes, Timothy Wigal

Bibliographic record

VenuePubMed · 2012
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsBridgepoint Active Healthcare
Fundersnot available
KeywordsCornishCategorical variableRating scalePsychologyStrengths and weaknessesAttention deficit hyperactivity disorderPsychiatryDevelopmental psychologyComputer sciencePhilosophySocial psychologyLinguisticsMachine learning
DOInot available

Abstract

fetched live from OpenAlex

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(Swanson, Wigal, & Lakes, 2009) and ethical (Swanson, Wigal, Lakes, &Volkow, 2011) issues has convinced us that the unpublished article is still relevant after more than a decade, so it is presented here with some minor updates.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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.037
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.037
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.006
Science and technology studies0.0010.003
Scholarly communication0.0020.004
Open science0.0020.003
Research integrity0.0010.007
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.092
GPT teacher head0.292
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations535
Published2012
Admission routes1
Has abstractyes

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