Computerized Neuropsychological Assessment Devices: Joint Position Paper of the American Academy of Clinical Neuropsychology and the National Academy of Neuropsychology
Bibliographic record
Abstract
This joint position paper of the American Academy of Clinical Neuropsychology and the National Academy of Neuropsychology sets forth our position on appropriate standards and conventions for computerized neuropsychological assessment devices (CNADs). In this paper, we first define CNADs and distinguish them from examiner-administered neuropsychological instruments. We then set forth position statements on eight key issues relevant to the development and use of CNADs in the healthcare setting. These statements address (a) device marketing and performance claims made by developers of CNADs; (b) issues involved in appropriate end-users for administration and interpretation of CNADs; (c) technical (hardware/software/firmware) issues; (d) privacy, data security, identity verification, and testing environment; (e) psychometric development issues, especially reliability, and validity; (f) cultural, experiential, and disability factors affecting examinee interaction with CNADs; (g) use of computerized testing and reporting services; and (h) the need for checks on response validity and effort in the CNAD environment. This paper is intended to provide guidance for test developers and users of CNADs that will promote accurate and appropriate use of computerized tests in a way that maximizes clinical utility and minimizes risks of misuse. The positions taken in this paper are put forth with an eye toward balancing the need to make validated CNADs accessible to otherwise underserved patients with the need to ensure that such tests are developed and utilized competently, appropriately, and with due concern for patient welfare and quality of care.
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 | 0.066 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.022 | 0.034 |
| Insufficient payload (model declined to judge) | 0.009 | 0.017 |
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".