Trial 1 versus Trial 2 of the Test of Memory Malingering: Evaluating accuracy without a “gold standard”.
Bibliographic record
Abstract
This study examines the accuracy of the Test of Memory Malingering (TOMM), a frequently administered measure for evaluating effort during neurocognitive testing. In the last few years, several authors have suggested that the initial recognition trial of the TOMM (Trial 1) might be a more useful index for detecting feigned or exaggerated impairment than Trial 2, which is the source for inference recommended by the original instruction manual (Tombaugh, 1996). We used latent class modeling (LCM) implemented in a Bayesian framework to evaluate archival Trial 1 and Trial 2 data collected from 1,198 adults who had undergone outpatient forensic evaluations. All subjects were tested with 2 other performance validity tests (the Word Memory Test and the Computerized Assessment of Response Bias), and for 70% of the subjects, data from the California Verbal Learning Test-Second Edition Forced Choice trial were also available. Our results suggest that not even a perfect score on Trial 1 or Trial 2 justifies saying that an evaluee is definitely responding genuinely, although such scores imply a lower-than-base-rate probability of feigning. If one uses a Trial 2 cut-off higher than the manual's recommendation, Trial 2 does better than Trial 1 at identifying individuals who are almost certainly feigning while maintaining a negligible false positive rate. Using scores from both trials, one can identify a group of definitely feigning and very likely feigning subjects who comprise about 2 thirds of all feigners; only 1% of the members of this group would not be feigning. (PsycINFO Database Record
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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 | 0.020 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".