The classification accuracy of Warrington’s recognition memory test (words) as a performance validity Test in a neurorehabilitation setting
Bibliographic record
Abstract
This study was designed to evaluate the classification accuracy of the Warrington’s Recognition Memory Test (RMT) in 167 patients (97 or 58.1% men; MAge = 40.4; MEducation= 13.8) medically referred for neuropsychological evaluation against five psychometrically defined criterion groups. At the optimal cutoff (≤42), the RMT produced an acceptable combination of sensitivity (.36-.60) and specificity (.85-.95), correctly classifying 68.4-83.3% of the sample. Making the cutoff more conservative (≤41) improved specificity (.88-.95) at the expense of sensitivity (.30-.60). Lowering the cutoff to ≤40 achieved uniformly high specificity (.91-.95) but diminished sensitivity (.27-.48). RMT scores were unrelated to lateral dominance, education, or gender. The RMT was sensitive to a three-way classification of performance validity (Pass/Borderline/Fail), further demonstrating its discriminant power. Despite a notable decline in research studies focused on its classification accuracy within the last decade, the RMT remains an effective free-standing PVT that is robust to demographic variables. Relatively low sensitivity is its main liability. Further research is needed on its cross-cultural validity (sensitivity to limited English proficiency).
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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".