This will only take a minute: Time cutoffs are superior to accuracy cutoffs on the forced choice recognition trial of the Hopkins Verbal Learning Test – Revised
Bibliographic record
Abstract
Objective This study was designed to evaluate the classification accuracy of the recently introduced forced-choice recognition trial to the Hopkins Verbal Learning Test – Revised (FCRHVLT-R) as a performance validity test (PVT) in a clinical sample. Time-to-completion (T2C) for FCRHVLT-R was also examined.Method Forty-three students were assigned to either the control or the experimental malingering (expMAL) condition. Archival data were collected from 52 adults clinically referred for neuropsychological assessment. Invalid performance was defined using expMAL status, two free-standing PVTs and two validity composites.Results Among students, FCRHVLT-R ≤11 or T2C ≥45 seconds was specific (0.86–0.93) to invalid performance. Among patients, an FCRHVLT-R ≤11 was specific (0.94–1.00), but relatively insensitive (0.38–0.60) to non-credible responding0. T2C ≥35 s produced notably higher sensitivity (0.71–0.89), but variable specificity (0.83–0.96). The T2C achieved superior overall correct classification (81–86%) compared to the accuracy score (68–77%). The FCRHVLT-R provided incremental utility in performance validity assessment compared to previously introduced validity cutoffs on Recognition Discrimination.Conclusions Combined with T2C, the FCRHVLT-R has the potential to function as a quick, inexpensive and effective embedded PVT. The time-cutoff effectively attenuated the low ceiling of the accuracy scores, increasing sensitivity by 19%. Replication in larger and more geographically and demographically diverse samples is needed before the FCRHVLT-R can be endorsed for routine clinical application.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".