Assessment of Intellectual Functioning among Children with Neurodevelopmental Disorders: Challenges and Implications Beyond the Clinical Practice
Bibliographic record
Abstract
Early and precise identification of neurodevelopmental disorders together with provision of adequate and timely interventions remain increasingly important tasks for multidisciplinary clinical teams. А central component in this process of comprehensive clinical evaluation is the assessment of children’s intellectual functioning. Intelligence test results represent a central component in the decision making process of determining a child’s future in terms of: qualification for special education, access to social welfare support, placement in therapeutic programs, etc. Clinical results yielded through the application of standardized intelligence assessment instruments (WISC, K-ABC, RPM), and especially the overall quantitative measure of cognitive ability (IQ) have become a central, and sometimes the only measure taken into consideration when determining a child’s level of functioning. Together with some distinct benefits, this practice places many children at risk of being underestimated and calls for revision and modification of standard assessment procedures. In line with these considerations, authors raise for discussion traditional approaches to diagnostics of intellectual functioning, highlighting some challenges, emerging from the constitutive particularities in the cognitive functioning of children with neurodevelopmental disorders. An attempt for identifying areas for further improvement alongside with research-informed recommendations for a contemporary, individualized and sensitive to the specifications of children with neurodevelopmental disorders assessment practice are outlined at the end of this paper. According to the authors’ opinion, exploration of the topic provides an important opportunity to advance the understanding of clinicians, primary healthcare professionals, educators and other professionals involved in supporting children with developmental deficits.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".