A Model to Guide the Conceptualization, Assessment, and Diagnosis of Nonverbal Learning Disorder
Bibliographic record
Abstract
Although many learning disability types are formally recognized in major classification systems such as DSM-IV-TR and ICD-10, Nonverbal Learning Disorder (NLD) is not despite over 40 years of literature addressing its theoretical and neuropsychological foundation, its major features, and the methods by which to assess and diagnose it. Currently, there is no general agreement regarding the defining features of NLD and their relationship to daily functioning. Presented here is a description of NLD in the context of a model aimed at providing a standard language and framework for describing health and health-related states: the International Classification of Functioning, Disability and Health (ICF). It is proposed that the ICF model can guide clinical thinking, professional practice—including assessment, diagnosis, and treatment—and research with respect to NLD, eventually leading to the inclusion of NLD in formal classification systems.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".