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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it