Can Malingering Be Identified With the Judgment of Line Orientation Test?
Bibliographic record
Abstract
The purpose of this study was to evaluate recently proposed (Meyers, Galinsky, & Volbrecht, 1999) cutoff scores for biased responding on the Judgment of Line Orientation Test (JLO). A large sample of individuals involved in head injury litigation (N = 294) took the JLO and 2 tests designed to detect biased responding, the Computerized Assessment ofResponse Bias (CARB) and the Word Memory Test (WMT), as part ofa comprehensive neuropsychological evaluation. Patients were divided into groups on the basis of brain injury severity and whether or not they scored in the suspicious range on the CARB or WMT. The patients who were identified as providing biased responding on the CARB or WMT also scored significantly lower on the JLO. However, the Meyers et al. (1999) cutoff score correctly identified only 9.9% ofthis group, with a 1% possible false-positive rate. A different cutoff score was selected that had .22 sensitivity and .96 specificity. Overall, these results suggest that the JLO has limited utility as a screenfor biased responding; however, clinicians are encouraged to evaluate these scores carefully if they do not seem to make biological or psychometric sense.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".