Improving Test Interpretation for Detecting Executive Dysfunction in Adults and Older Adults: Prevalence of Low Scores on the Test of Verbal Conceptualization and Fluency
Bibliographic record
Abstract
Knowing the prevalence of low scores on a battery of executive-functioning tests supplements clinical interpretation and can reduce the likelihood of misdiagnosing deficits in executive functioning. The purpose of this study is to examine the base rates of low scores on the Test of Verbal Conceptualization and Fluency (TVCF; Reynolds & Horton, 2006 ) in healthy adults (n = 332; M (age) = 33.0 years, SD = 10.5, range = 20-59) and older adults (n = 138; M (age) = 74.9 years, SD = 7.8, range = 60-89) from the TVCF standardization sample. The TVCF consists of four tests of executive functioning (i.e., Category Fluency, Letter Naming, Classification, and Trails C) that provide five age-adjusted T-scores. The prevalence of low scores was examined in the total sample and was stratified by educational level. When the five T-scores were considered simultaneously, having one or more scores that were 1 standard deviation (SD) below the mean was found in 28% of healthy adults and 38% of older adults. Education-based differences were also present with more lenient cutoff scores (e.g., 1 SD) but not with more conservative cutoffs. Consistent with the existing literature on other test batteries, at least one low subtest score on the TVCF is common in healthy adults and older adults.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| 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".