Comparing Actual to Estimated Base Rates of "Abnormal" Scores on Neuropsychological Test Batteries: Implications for Interpretation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it