Base rates of low neuropsychological test scores in older adults with subjective cognitive impairment: Findings from a tertiary memory clinic
Bibliographic record
Abstract
Base rates of low scores are typically determined from normative samples, which differ from clinical samples. We examined base rates of spuriously low scores for 93 older adults with subjective cognitive impairment presenting to a memory clinic. Crawford's Monte Carlo simulation algorithm was used to estimate multivariate base rates by calculating the percentage of cognitively intact memory clinic patients who produced normed scores at or below the 5th percentile. Neuropsychological tests included: Weschler Adult Intelligence Scale block design, digit span backwards, coding, Weschler Memory Scale logical memory immediate/delayed; California Verbal Learning Test immediate/delayed memory; Brief Visuospatial Memory Test immediate/delayed; and Delis-Kaplan Executive Functioning category switching, letter number sequencing, and inhibition/switching. An estimated 33.58% of the cognitively intact memory clinic population would have one or more low scores, 14.7% two or more, 6.55% three or more, 2.94% four or more, and 1.31% 5 or more due to chance. Base rates were then applied to a subset of clinical data: all with dementia and most with MCI had low scores that exceeded the base rates. Determining base rates of spuriously low scores on a neuropsychological battery in clinical samples could reduce false positives by using empirical adjustments for expected low scores.
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 | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".