Impact of criterion measures on the classification accuracy of TOMM-1
Bibliographic record
Abstract
This study was designed to examine the effect of various criterion measures on the classification accuracy of Trial 1 of the Test of Memory Malingering (TOMM-1), a free-standing performance validity test (PVT). Archival data were collected from a case sequence of 91 (MAge = 42.2 years; MEducation = 12.7) patients clinically referred for neuropsychological assessment. Trials 2 and Retention of the TOMM, the Word Choice Test, and three validity composites were used as criterion PVTs. Classification accuracy varied systematically as a function of criterion PVT. TOMM-1 ≤ 43 emerged as the optimal cutoff, resulting in a wide range of sensitivity (.47–1.00), with perfect overall specificity. Failing the TOMM-1 was unrelated to age, education or gender, but was associated with elevated self-reported depression. Results support the utility of TOMM-1 as an independent, free-standing, single-trial PVT. Consistent with previous reports, the choice of criterion measure influences parameter estimates of the PVT being calibrated. The methodological implications of modality specificity to PVT research and clinical/forensic practice should be considered when evaluating cutoffs or interpreting scores in the failing range.
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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 | 0.069 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".