Detecting Exaggeration and Malingering With the Trail Making Test
Bibliographic record
Abstract
The purpose of this study was to examine whether unusual performance on the Trail Making Test could be indicative of deliberate exaggeration. Participants were 571 patients seen as part of a hospital trauma service who had acute traumatic brain injuries, and 228 patients involved in head injury litigation. As expected, the hospital patients with more severe traumatic brain injuries performed more poorly than the patients with less severe brain injuries on Trails A and Trails B. Cutoff score tables were developed for the patients with acute traumatic brain injuries for the total sample and by injury severity groups. Scores falling at or below the 5th percentile were considered suspicious for possible exaggeration. The performances of the head injury litigants who exaggerated on at least one well-validated symptom validity test were compared to these cutoffs. Very high positive predictive values for individuals with very mild head injuries on Trails A and B were identified (i.e., both 100%); lower positive predictive values were obtained for individuals with more severe head injuries (55.6-60%). The negative predictive values were only moderate (range=66.4-78.2%), and the sensitivity was very low (range = 7.1-18.5%) for all groups. Scores that fall in the range of possible biased responding should be considered "red flags" for the clinician because they likely do not make biological or psychometric sense. However, the sensitivity of the test for deliberate exaggeration is very low, so clinicians who rely on this test in isolation to identify deliberately poor performance will fail to identify the vast majority of cases.
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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 | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".