Verbal fluency and digit span variables as performance validity indicators in experimentally induced malingering and real world patients with TBI
Bibliographic record
Abstract
Objective: This study was designed to examine the classification accuracy of verbal fluency (VF) measures as performance validity tests (PVT).Method: Student volunteers were assigned to the control (n = 57) or experimental malingering (n = 24) condition. An archival sample of 77 patients with TBI served as a clinical comparison.Results: Among students, FAS T-score ≤29 produced a good combination of sensitivity (.40–.42) and specificity (.89–.95). Animals T-score ≤31 had superior sensitivity (.53–.71) at .86-.93 specificity. VF tests performed similarly to commonly used PVTs embedded within Digit Span: RDS ≤7 (.54–.80 sensitivity at .93–.97 specificity) and age-corrected scaled score (ACSS) ≤6 (.54–.67 sensitivity at .94–.96 specificity). In the clinical sample, specificity was lower at liberal cutoffs [animals T-score ≤31 (.89–.91), RDS ≤7 (.86–.89) and ACSS ≤6 (.86–.96)], but comparable at conservative cutoffs [animals T-score ≤29 (.94–.96), RDS ≤6 (.95–.98) and ACSS ≤5 (.92–.96)].Conclusions: Among students, VF measures had higher signal detection performance than previously reported in clinical samples, likely due to the absence of genuine impairment. The superior classification accuracy of animal relative to letter fluency was replicated. Results suggest that existing validity cutoffs can be extended to cognitively high functioning examinees, and emphasize the importance of population-specific cutoffs.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".