Comparing Actual to Estimated Base Rates of "Abnormal" Scores on Neuropsychological Test Batteries: Implications for Interpretation
Bibliographic record
Abstract
Clinicians can use the prevalence of low scores to help interpret test performance. However, this information is limited for most test batteries. In 2007, Crawford, Garthwaite, and Gault presented Monte Carlo simulation software for estimating the base rates of low scores for any battery of tests. The purpose of this study is to examine the accuracy of a Monte Carlo simulation program for estimating the base rates of low scores. Base rates of low scores were: (a) calculated from large normative samples (actual base rates) for the Neuropsychological Assessment Battery and the Wechsler Adult Intelligence Scale-III/Wechsler Memory Scale-Third Edition and compared to (b) Monte Carlo estimations (estimated base rates). Monte Carlo estimations of the base rates of low scores had good accuracy when compared with the actual base rates of low scores for the two batteries. However, estimated base rates lose considerable accuracy in those with low or high intelligence. Monte Carlo simulation software is a potential option for clinicians to compute the base rates of low scores for any battery with published intercorrelations. However, the Monte Carlo program underestimates the base rates for those with low intelligence and overestimates the base rates for those with high intelligence.
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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 | 0.083 | 0.490 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".