Estimating Base Rates of Impairment in Neuropsychological Test Batteries: A Comparison of Quantitative Models
Bibliographic record
Abstract
Neuropsychologists frequently rely on a battery of neuropsychological tests which are normally distributed to determine impaired functioning. The statistical likelihood of Type I error in clinical decision-making is in part determined by the base rate of normative individuals obtaining atypical performance on neuropsychological tests. Base rates are most accurately obtained by co-normed measures, but this is rarely accomplished in neuropsychological testing. Several statistical methods have been proposed to estimate base rates for tests that are not co-normed. This study compared two statistical approaches (binomial and Monte Carlo models) used to estimate the base rates for flexible test batteries. The two approaches were compared against empirically derived base rates for a multitest co-normed battery of cognitive measures. Estimates were compared across a variety of conditions including age and different α levels (N =3,356). Monte Carlo R(2) estimates ranged from .980 to .997 across five different age groups, indicating a good fit. In contrast, the binomial model fit estimates ranged from 0.387 to 0.646. Results confirm that the binomial model is insufficient for estimating base rates because it does not take into account correlations among measures in a multitest battery. Although the Monte Carlo model produced more accurate results, minor biases occurred that are likely due to skewess and kurtosis of test variables. Implications for future research and applied practice are discussed.
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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 | 0.107 | 0.311 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".