Replicating the classification accuracy of the Verbal Paired Associates and Visual Reproduction recognition trials as embedded performance validity tests.
Bibliographic record
Abstract
OBJECTIVE: This study was designed to replicate previous research on the clinical utility of the Verbal Paired Associates (VPA) and Visual Reproduction (VR) subtests of the WMS-IV as embedded performance validity tests (PVTs) and perform a critical item (CR) analysis within the VPA recognition trial. METHOD: = 13.9). Classification accuracy was computed against psychometrically defined criterion groups based on the outcome of various free-standing and embedded PVTs. RESULTS: Age-corrected scaled scores ≤ 6 were specific (.89-.98) but had variable sensitivity (.36-.64). A VPA recognition cutoff of ≤ 34 produced a good combination of sensitivity (.46-.56) and specificity (.92-.93), as did a VR recognition cutoff of ≤ 4 (.48-.53 sensitivity at .86-.94 specificity). Critical item analysis expanded the VPA's sensitivity by 3.5%-7.0% and specificity by 5%-8%. Negative learning curves (declining output on subsequent encoding trials) were rare but highly specific (.99-1.00) to noncredible responding. CONCLUSIONS: Results largely support previous reports on the clinical utility of the VPA and VR as embedded PVTs. Sample-specific fluctuations in their classification accuracy warrant further research into the generalizability of the findings. Critical item analysis offers a cost-effective method for increasing confidence in the interpretation of the VPA recognition trial as a PVT. (PsycInfo Database Record (c) 2024 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 | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".