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International Consensus on Standard Outcome Measures for Neurodevelopmental Disorders

2024· article· en· W4399626081 on OpenAlexaff
Melissa Mulraney, Umanga de Silva, Andria Joseph, Maria da Luz Sousa Fialho, Iain Dutia, Natalie Munro, Jonathan M. Payne, Tobias Banaschewski, Cláudia Bandeira de Lima, Mark A. Bellgrove, Samuel R. Chamberlain, Phyllis Chan, Ivy Chong, Alison Clink, Samuele Cortese, Eileen Daly, Stephen V. Faraone, Melissa Gladstone, Adam J. Guastella, Juulia Järvdike, Sidra Kaleem, Mark G. Lovell, Tamasin Meller, Péter Nagy, Jeffrey H. Newcorn, Guilherme V. Polanczyk, Emily Simonoff, Peter Szatmari, Caroline Tehan, Karin S. Walsh, Susan Wamithi, David Coghill

Bibliographic record

VenueJAMA Network Open · 2024
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersServierUniversity of OxfordUniversity of Cambridge
KeywordsDelphi methodCLARITYSet (abstract data type)PsychologyRating scaleScale (ratio)Mental healthClinical psychologyService (business)MedicinePsychiatryApplied psychologyDevelopmental psychologyComputer science

Abstract

fetched live from OpenAlex

Importance: The use of evidence-based standardized outcome measures is increasingly recognized as key to guiding clinical decision-making in mental health. Implementation of these measures into clinical practice has been hampered by lack of clarity on what to measure and how to do this in a reliable and standardized way. Objective: To develop a core set of outcome measures for specific neurodevelopmental disorders (NDDs), such as attention-deficit/hyperactivity disorder (ADHD), communication disorders, specific learning disorders, and motor disorders, that may be used across a range of geographic and cultural settings. Evidence Review: An international working group composed of clinical and research experts and service users (n = 27) was convened to develop a standard core set of accessible, valid, and reliable outcome measures for children and adolescents with NDDs. The working group participated in 9 video conference calls and 8 surveys between March 1, 2021, and June 30, 2022. A modified Delphi approach defined the scope, outcomes, included measures, case-mix variables, and measurement time points. After development, the NDD set was distributed to professionals and service users for open review, feedback, and external validation. Findings: The final set recommends measuring 12 outcomes across 3 key domains: (1) core symptoms related to the diagnosis; (2) impact, functioning, and quality of life; and (3) common coexisting problems. The following 14 measures should be administered at least every 6 months to monitor these outcomes: ADHD Rating Scale 5, Vanderbilt ADHD Diagnostic Rating Scale, or Swanson, Nolan, and Pelham Rating Scale IV; Affective Reactivity Index; Children's Communication Checklist 2; Colorado Learning Disabilities Questionnaire; Children's Sleep Habits Questionnaire; Developmental-Disability Children's Global Assessment Scale; Developmental Coordination Disorder Questionnaire; Family Strain Index; Intelligibility in Context Scale; Vineland Adaptive Behavior Scale or Repetitive Behavior Scale-Revised and Social Responsiveness Scale; Revised Child Anxiety and Depression Scales; and Yale Global Tic Severity Scale. The external review survey was completed by 32 professionals and 40 service users. The NDD set items were endorsed by more than 70% of professionals and service users in the open review survey. Conclusions and Relevance: The NDD set covers outcomes of most concern to patients and caregivers. Use of the NDD set has the potential to improve clinical practice and research.

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)0.241
metaresearch head score (Gemma)0.262
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.759
Threshold uncertainty score0.936

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2410.262
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.014
Bibliometrics0.0130.009
Science and technology studies0.0030.005
Scholarly communication0.0060.005
Open science0.0140.010
Research integrity0.0100.015
Insufficient payload (model declined to judge)0.0050.004

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 head0.075
GPT teacher head0.380
Teacher spread0.306 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreMethods

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

Citations16
Published2024
Admission routes1
Has abstractyes

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