The classification accuracy of the recognition memory test–words as a performance validity test is affected by gender and education.
Bibliographic record
Abstract
OBJECTIVE: Previous research suggested that Recognition Memory Test-Words (RMT-W) scores may be confounded by gender and handedness. This study was designed to examine its classification accuracy as a performance validity test (PVT) and susceptibility to demographic characteristics. METHOD: = 12.9). The RMT-W's classification accuracy was computed against psychometrically operationalized criterion groups. RESULTS: Optimal RMT-W cutoffs (≤ 42 to ≤ 40) produced a good combination of sensitivity (.62-.70) and specificity (.90-.96), correctly classifying 85.6%-87.7% of the sample. Women scored 1.5 points higher. RMT-W scores were unrelated to handedness but were correlated with education. A linear relationship emerged between level of education and the cutoff needed to achieve ≥ .90 specificity: ≤ 43 for ≥ 13 years of education, ≤ 41 for 12 years of education, and ≤ 39 for ≤ 11 years of education. RMT-W ≤ 45 had .91 specificity in women with postsecondary education. CONCLUSIONS: Results suggest that overall, the RMT-W remains an effective free-standing PVT. Gender, age, and handedness in isolation had minimal impact on RMT-W scores. However, education had a clinically significant effect. The combined effect of gender and education produced a marked shift in classification accuracy. Systematic research is needed on the relationship between demographics and PVT outcomes to ensure that cutoffs have the same clinical interpretation regardless of patient variables. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".