Empirical Derivation and Validation of a Clinical Case Definition for Neuropsychological Impairment in Children and Adolescents
Bibliographic record
Abstract
Neuropsychological assessment aims to identify individual performance profiles in multiple domains of cognitive functioning; however, substantial variation exists in how deficits are defined and what cutoffs are used, and there is no universally accepted definition of neuropsychological impairment. The aim of this study was to derive and validate a clinical case definition rule to identify neuropsychological impairment in children and adolescents. An existing normative pediatric sample was used to calculate base rates of abnormal functioning on eight measures covering six domains of neuropsychological functioning. The dataset was analyzed by varying the range of cutoff levels [1, 1.5, and 2 standard deviations (SDs) below the mean] and number of indicators of impairment. The derived rule was evaluated by bootstrap, internal and external clinical validation (orthopedic and traumatic brain injury). Our neuropsychological impairment (NPI) rule was defined as "two or more test scores that fall 1.5 SDs below the mean." The rule identifies 5.1% of the total sample as impaired in the assessment battery and consistently targets between 3 and 7% of the population as impaired even when age, domains, and number of tests are varied. The NPI rate increases in groups known to exhibit cognitive deficits. The NPI rule provides a psychometrically derived method for interpreting performance across multiple tests and may be used in children 6-18 years. The rule may be useful to clinicians and scientists who wish to establish whether specific individuals or clinical populations present within expected norms versus impaired function across a battery of neuropsychological tests.
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 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 | 0.056 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".