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Record W4212896307 · doi:10.1111/jir.12924

An international field study of the ICD‐11 behavioural indicators for disorders of intellectual development

2022· article· en· W4212896307 on OpenAlexaff
Kyle R. Lemay, C. S. Kogan, T. J. Rebello, Jared W. Keeley, Rachna Bhargava, Pratap Sharan, Manoj Sharma, John Vijay Sagar Kommu, M. Thomas Kishore, Jair de Jesus Mari, P. Ginige, Serafino Buono, Marilena Recupero, Marinella Zingale, Tommasa Zagaria, S. Cooray, Ashok Roy, Geoffrey M. Reed

Bibliographic record

VenueJournal of Intellectual Disability Research · 2022
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIntellectual disabilityPsychologyAdaptive behaviourVineland Adaptive Behavior ScalePsychiatryMini-international neuropsychiatric interviewAdaptive functioningMental healthClinical psychologyReliability (semiconductor)Borderline intellectual functioningBehavioural disordersPediatricsMedicineAdaptive behaviorDevelopmental psychologyCognition

Abstract

fetched live from OpenAlex

BACKGROUND: The World Health Organization (WHO) has approved the 11th Revision of the International Classification of Diseases (ICD-11). A version of the ICD-11 for Mental, Behavioural and Neurodevelopmental Disorders for use in clinical settings, called the Clinical Descriptions and Diagnostic Requirements (CDDR), has also been developed. The CDDR includes behavioural indicators (BIs) for assessing the severity of disorders of intellectual development (DID) as part of the section on neurodevelopmental disorders. Reliable and valid diagnostic assessment measures are needed to improve identification and treatment of individuals with DID. Although appropriately normed, standardised intellectual and adaptive behaviour assessments are considered the optimal assessment approach in this area, they are unavailable in many parts of the world. This field study tested the BIs internationally to assess the inter-rater reliability, concurrent validity, and clinical utility of the BIs for the assessment of DID. METHODS: This international study recruited a total of 206 children and adolescents (5-18 years old) with a suspected or established diagnosis of DID from four sites across three countries [Sri-Lanka (n = 57), Italy (n = 60) and two sites in India (n = 89)]. Two clinicians assessed each participant using the BIs with one conducting the clinical interview and the other observing. Diagnostic formulations using the BIs and clinical utility ratings were collected and entered independently after each assessment. At a follow-up appointment, standardised measures (Leiter-3, Vineland Adaptive Behaviour Scales-II) were used to assess intellectual and adaptive abilities. RESULTS: The BIs had excellent inter-rater reliability (intra-class correlations ranging from 0.91 to 0.97) and good to excellent concurrent validity (intra-class correlations ranging from 0.66 to 0.82) across sites. Compared to standardised measures, the BIs had more diagnostic overlap between intellectual and adaptive functioning. The BIs were rated as quick and easy to use and applicable across severities; clear and understandable with adequate to too much level of detail and specificity to describe DID; and useful for treatment selection, prognosis assessments, communication with other health care professionals, and education efforts. CONCLUSION: The inclusion of newly developed BIs within the CDDR for ICD-11 Neurodevelopmental Disorders must be supported by information on their reliability, validity, and clinical utility prior to their widespread adoption for international use. BIs were found to have excellent inter-rater reliability, good to excellent concurrent validity, and good clinical utility. This supports use of the BIs within the ICD-11 CDDR to assist with the accurate identification of individuals with DID, particularly in settings where specialised services are unavailable.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.121
GPT teacher head0.433
Teacher spread0.312 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations12
Published2022
Admission routes1
Has abstractyes

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